Modern applications rely heavily on APIs to connect different services, manage data flow, and enable seamless user experiences. However, traditional testing approaches often treat API and UI testing as separate processes, leading to:

• Fragmented testing workflows that require multiple tools and platforms
• Delayed feedback loops when backend issues aren’t caught early
• Incomplete test coverage that misses critical API-UI interactions
• Resource inefficiency from managing separate testing environments

LambdaTest KaneAI bridges this gap by offering seamless API testing capabilities alongside its powerful UI testing features, providing development teams with a unified testing platform.

Unified API Testing with KaneAI

LambdaTest KaneAI addresses these challenges by integrating API testing directly into your existing test workflows. This unified approach enables teams to validate both frontend functionality and backend services within a single platform, streamlining the entire testing process.

Note

Check out the detailed support documentation to get started with API testing using KaneAI.

What are the Benefits of API testing?

API testing offers significant advantages in terms of efficiency, system security, and overall product quality. Here are some key benefits of incorporating API testing into your development workflow:

• Faster Releases: While GUI tests can be time-consuming, API testing accelerates the process, saving you up to eight hours of testing time. This allows your team to focus on other critical aspects of software development and enhances the overall speed of your product release.
• Improved Test Coverage: API testing dives deeper than just the user interface by validating the core system components, such as database interactions. By testing APIs, you ensure that all layers of the application are functioning properly, leading to better coverage and, ultimately, higher-quality software and more satisfied users.
• Shift-Left Testing Made Easy: API testing can be implemented early in the development cycle without needing a GUI. Developers can quickly run tests, get real-time feedback, and resolve issues in the early stages. Compared to UI tests, API tests are typically completed in seconds or minutes, offering faster insights into the system’s performance.
• Minimal Maintenance: Since changes to API layers are infrequent and typically tied to major updates in business logic, API testing requires less ongoing maintenance. By reflecting the intended API specifications from the start, you can ensure the system remains stable, even as updates are made.
• Faster Bug Detection and Resolution: With the speed of API testing, bugs are detected and diagnosed earlier in the development process. Immediate feedback means quicker bug fixes, preventing delays and improving the overall efficiency of your development cycle.
• Lower Testing Costs: The comprehensive nature of API testing, along with its faster execution and easier maintenance, significantly reduces the overall cost of testing. This enables you to reallocate resources to other high-priority areas, improving ROI.
• Cross-Language Compatibility: API testing supports data exchange in XML and JSON formats, meaning you can use a variety of programming languages, including JavaScript, Java, Ruby, Python, or PHP. This flexibility ensures your team can test APIs using the tools and languages they are most comfortable with.

By incorporating API testing into your workflow, especially with advanced tools like KaneAI, you can achieve faster, more efficient, and cost-effective testing, all while ensuring a more secure and higher-quality product.

Demo of API testing with a Real-World Use Case:

Let’s explore how a development team can leverage KaneAI’s API testing capabilities using a practical e-commerce scenario. We’ll use the PetStore API as our example to demonstrate comprehensive backend testing.

Setting Up Your API Testing Environment

Initializing API Testing by creating a web test and adding APIs through KaneAI’s intuitive slash command interface.

Rapid API configuration with cURL commands helps automatically configure API settings by simply pasting your cURL command into the designated area, where KaneAI intelligently populates all necessary details, including headers, parameters, and request bodies, eliminating manual configuration errors and significantly speeding up the test setup.

Validating API Responses for Reliability

KaneAI’s validation feature ensures your APIs respond correctly before incorporating them into your test suite.

When you click ‘validate,’ the system checks the API response and automatically adds successful requests (those returning a 200 status) to your test steps. This automated validation prevents faulty APIs from entering your test pipeline.

Handling Edge Cases and Error Scenarios

Real-world testing isn’t just about happy paths. KaneAI allows you to manually add APIs that return non-200 status codes, such as 400 Bad Request responses. This capability is crucial for testing error handling, validation logic, and edge cases that your application must gracefully manage.negative testing/negative workflows

Streamlining Testing with Batch Processing

For complex applications with multiple API endpoints, KaneAI offers batch processing capabilities. You can add multiple APIs simultaneously by clicking the plus icon and selecting each endpoint, or paste multiple cURL commands for automatic addition. This feature is particularly valuable when testing integrated workflows that involve multiple service calls.

Modern APIs utilize various HTTP methods for different operations. KaneAI supports the full spectrum of HTTP methods including POST for creating resources, PUT for updates, GET for retrieval, and DELETE for removal. This comprehensive support ensures you can test complete CRUD operations and complex API interactions.

How to get Execution and Performance Insights on API Testing?

Once your API test suite is configured, KaneAI enables simultaneous execution of all added APIs. This parallel execution approach provides immediate insights into API performance, response times, and data integrity across your entire backend infrastructure.

The execution results include detailed information about:

• HTTP methods used for each request
• Response status codes and their meanings
• Execution times for performance analysis
• Response data for validation and debugging

Note

Kickstart Your API Testing with KaneAI. Get Started Now!

Best Practices for API Testing

API testing involves several best practices, including logically structuring your tests, leveraging validation features, testing beyond success scenarios, and monitoring performance metrics. These practices help make your API tests more efficient, reliable, and resilient.

Structure Your Tests Logically

Organize your API tests to mirror your application’s workflows. Group related API calls together and ensure your test sequence matches real-user interactions.

Leverage Validation Features

Always validate API responses before adding them to your test steps. This practice ensures your test suite remains reliable and catches regressions effectively.

Test Beyond Success Scenarios

Include error cases and edge conditions in your test suite. Testing how your APIs handle invalid inputs and error states is crucial for building resilient applications.

Monitor Performance Metrics

Use KaneAI’s execution time data to establish performance baselines and identify potential bottlenecks in your API infrastructure.

All in All!

LambdaTest KaneAI’s API testing capabilities represent a significant advancement in comprehensive testing strategies. By providing a unified platform for both UI and API testing, KaneAI enables development teams to build more reliable applications while optimizing their testing workflows.

The integration of API testing into existing test suites, combined with features like automated configuration, batch processing, and comprehensive HTTP method support, makes KaneAI an invaluable tool for modern development teams.

Happy Testing!

AI agents often struggle with memory limitations, causing errors, inconsistent outputs, and overlooked details. Context Engineering addresses this challenge by defining what AI should remember, how it retrieves information, and how it manages context efficiently.

Part 1 of this Context Engineering series explains why AI forgets, the four common failure modes, and practical strategies, WRITE and SELECT, for structuring, storing, and retrieving information. These approaches ensure reliable reasoning, accurate outputs, and scalable performance across complex, multi-step AI tasks.

The AI Memory Problem: An Introduction

Consider you’re at a restaurant, and the waiter has a wonderful memory. They recall:

• The 50-page menu has every dish on it.
• All of the conversations with customers from the past week.
• Every request that was made that was special.
• The ingredients in each dish.
• Every order and table number.
• All ways to pay are available.

Sounds wonderful, doesn’t it? The problem is that when you ask, “What’s good for lunch?” the waiter stops. They have so much information that they can’t pay attention to your simple question. If you don’t do proper Context Engineering, this is what happens to AI agents.

AI agents are getting smarter and better at doing more complicated tasks, but they have a big problem: their “working memory” is limited. AI models can only hold so much information in their active context at any one time, just like the waiter who is too busy to help you. If you load them too much, they slow down, make mistakes, or even stop working altogether.

Context Engineering is the field that deals with controlling what AI agents remember, forget, and find at each step of their work.

What Is Context Engineering?

AI researchers define Context Engineering as the delicate art and science of filling the context window with just the right information for the next step.

Let’s look at what “context” means in AI to get a better idea of this:

Understanding the Context Window

A context window is the amount of text that modern Large Language Models (LLMs) can “pay attention to” when they make a response.

It’s like your computer’s RAM:

• The CPU is what gives the LLM its reasoning power.
• The RAM is the memory that is used to work (the context window).
• The hard drive is a place to store things for a long time (like documents and external databases).

As Andrej Karpathy explained, “LLMs are like a new kind of operating system. The LLM is like the CPU and its context window is like the RAM.”

What Goes Into Context?

AI context usually has three parts:

• Instructions
  • System prompts
  • Few-shot examples
  • Tool descriptions
  • Response format requirements
  • Behavioral guidelines
• Knowledge
  • Domain facts
  • Company policies
  • Historical data
  • User preferences
  • Retrieved documents
• Tools
  • Available function descriptions
  • API specifications
  • Previous tool call results
  • Error messages and feedback
Note

Test your AI agents across real-world scenarios. Try Agent to Agent Testing Today!

Why Context Engineering Matters: The Four Failure Modes?

When context isn’t handled correctly, AI agents break down in certain, predictable ways. Drew Breunig identified four critical failure modes that impact AI agents:

1. Context Poisoning

What happens: AI remembers a hallucination or mistake and keeps it in its memory between interactions.

The DeepMind team documented this problem statement in their Gemini 2.5 technical report while the AI was playing Pokémon:

“An especially egregious form of this issue can take place with ‘context poisoning’ – where many parts of the context (goals, summary) are ‘poisoned’ with misinformation about the game state, which can often take a very long time to undo.”

Real-world scenario:

Turn 1:
User: "My account number is 12345"
AI: "Got it, account 54321" (misheard)
AI saves to memory: account_number = 54321

Turn 2 (next day):
User: "Check my account"
AI: "Looking up account 54321..." (wrong every time)

Impact: The AI will always work with wrong information until someone fixes its memory. If the poisoned context changes the AI’s goals or strategy, it can “become fixated on achieving impossible or irrelevant goals.”

2. Context Distraction

What happens: AI gets too much information and starts to focus on things that aren’t important instead of the task at hand.

As Drew Breunig explains, “Context Distraction is when the context overwhelms the training.” The Gemini 2.5 team observed this while playing Pokémon:

“As the context grew significantly beyond 100k tokens, the agent showed a tendency toward favoring repeating actions from its vast history rather than synthesizing novel plans.”

Instead of using what it learnt to come up with new plans, the agent became obsessed with doing things it had done before in its long history.

Real-world scenario:

Context includes:
- 500 lines of user's purchase history
- 200 product descriptions
- 50 previous conversations

User asks: "What's my current cart total?"

AI responds: "You previously bought a blue sweater on March 3rd for $29.99,
and before that you looked at red shoes on February 28th..."
(Never actually answers the cart total!)

Impact: The AI gets distracted by accumulated context and either repeats past actions or focuses on irrelevant details instead of addressing the actual query.

3. Context Confusion

What happens: Superfluous context influences responses in unexpected ways.

Drew Breunig defines this as “when superfluous context influences the response”. The problem: “If you put something in the context, the model has to pay attention to it.”

Even more dramatic: when researchers gave a Llama 3.1 8B model a query with all 46 tools in the GeoEngine benchmark, it failed. However, when the team reduced the selection to just 19 relevant tools, the project succeeded – even though both options were well within the 16k context window.

Real-world scenario:

Context includes: "User is from Texas"

User asks: "What's a good barbecue sauce recipe?"
AI assumes: Texas-style BBQ
AI responds: "Here's a great Texas BBQ sauce recipe with..."

But user wanted: >Korean BBQ sauce (never specified because seemed obvious to them)

Impact: The AI gets sidetracked by all the context it has and either does the same thing again or focuses on things that aren’t relevant instead of answering the question.

4. Context Clash

What happens is that too much context changes how people respond in ways that are hard to predict.

Drew Breunig describes this as “when parts of the context disagree.”

Microsoft and Salesforce research documented this brilliantly. They took benchmark prompts and “sharded” their information across multiple chat turns, simulating how agents gather data incrementally. The results were dramatic: an average 39% drop in performance.

Why? Because “when LLMs take a wrong turn in a conversation, they get lost and do not recover.” The assembled context contains the AI’s early (incorrect) attempts at answering before it had all the information. These wrong answers remain in the context and poison the final response.

Real-world scenario:

Context piece 1: "Free shipping on orders over $50" (general policy)
Context piece 2: "No free shipping on furniture items" (category exception)
Context piece 3: "Holiday sale: Free shipping on everything!" (temporary promo)

User: "Do I get free shipping on this $75 chair?"

AI: "Uh... yes? No? Maybe? It depends on... wait..." (confused)

Impact: AI makes wrong guesses based on information that isn’t directly related. The model has to pay attention to even information that isn’t relevant in the context window, which hurts performance.

Anthropic’s research emphasises that “context is a critical but finite resource” that must be managed carefully to avoid these failures.

These four failure modes don’t just appear in research papers – they also show up in production environments every day, especially when multiple agents collaborate or pass work between each other. In these environments, even a small mismatch in shared memory or a context handoff can ripple through the chain, producing inconsistent behavior that’s hard to trace.

Testing each agent in isolation only catches part of the issue. You also need to see how they behave when integrated, when outputs from one become inputs for another, when reasoning chains overlap, or when context updates get out of sync. This is where agent testing becomes essential. It helps surface subtle issues like message drift, state misalignment, and reasoning divergence long before they reach production.

To test AI agents, consider using AI-native agentic cloud platforms like LambdaTest. It offers Agent to Agent Testing where you define multiple synthetic personas and simulate chat, voice, and multimodal interactions. They help you measure the same metrics (bias, hallucination, tone consistency) at scale, under varied input types and in handoffs between agents.

To get started, check out this LambdaTest Agent to Agent Testing guide.

The Four Pillars of Context Engineering

Top AI researchers and companies have agreed upon four basic ways to manage context well. This post will talk about the first two pillars. The last two will be covered in Part 2.

Pillar 1: WRITE (Structured Context Storage)

Core Principle: Don’t make AI remember everything that’s in active memory. Store data in formats that are easy to find and organized outside the context window.

Technique 1.1: Scratchpads

A scratchpad is a place where the AI can write down temporary notes while it works on a task.

How it works:

Think of it like sticky notes on a desk:

• Yellow sticky: “Don’t forget: Delta is the user’s favourite airline.”
• Blue sticky: “Important: The budget is $500 at most.”
• Green sticky: “To Do: Look up the prices of flights for October”

The AI can:

• When they learn something new, they should write it down on a sticky note.
• When you need to remember something, read old sticky notes.
• To understand what the sticky notes say, read them all. The AI doesn’t just remember everything; it writes things down and looks them up when it needs to.

Example Usage:

AI Task: "Analyze 5 research papers and synthesize findings"

Turn 1: Read Paper 1
  Scratchpad: "paper_1_key_finding: Machine learning improves accuracy by 15%"

Turn 2: Read Paper 2
  Scratchpad: "paper_2_key_finding: Neural networks require large datasets"

Turn 3: Read Paper 3
  Scratchpad: "paper_3_key_finding: Ensemble methods reduce overfitting"

Turn 4: Synthesize
  AI reads all scratchpad notes
  Context: >Only the key findings (not the full papers!)
  Generates: Comprehensive synthesis

Anthropic’s multi-agent research system uses this approach extensively: “The Lead Researcher begins by evaluating the method and saving the plan to Memory to maintain context, as exceeding 200,000 tokens in the context window will result in truncation, making it crucial to preserve the plan.”

Technique 1.2: Structured Memory Systems

AI agents need more than just temporary scratchpads; they need long-term memory that lasts between sessions.

There are three types of memory:

Semantic Memory (Facts & Knowledge)

Like knowing things about someone:

• “Sarah likes her coffee with no sugar and no cream.”
• “Sarah can’t eat peanuts because she’s allergic to them.”
• “Blue is Sarah’s favorite colour.”

The AI keeps track of things about you and what you like. It doesn’t remember when it learnt these things; it only knows that they are true.

Episodic Memory (Past Events)

Like remembering certain times:

• “We fixed the login problem last Tuesday by changing the password.”
• “We set up the new database two weeks ago.”
• “We tried approach A last month, but it didn’t work.”

The AI can remember certain events and what happened. Like your photo album, each memory has a date and a story.

Procedural Memory (How-To Knowledge)

Like knowing how to make a cake:

• Step 1: Combine the sugar and flour.
• Step 2: Put in the milk and eggs.
• Step 3: Put in the oven at 350°F for 30 minutes.

The AI knows how to do things. You follow the steps in a recipe book to get something done.

Key Design Principles:

• Schema-Driven: Use structured formats (JSON, databases), not free text.
• Time-Stamped: Track when information was added.
• Tagged: Enable filtering by category, importance, freshness.
• Versioned: Track changes to memories over time.
• Queryable: Support efficient lookup and retrieval.

Pillar 2: SELECT (Intelligent Context Retrieval)

The main idea is not to load everything. Get only what you need for the task at hand.

Technique 2.1: Retrieval-Augmented Generation (RAG)

RAG is probably the most important method in modern Context Engineering. It lets AI work with knowledge bases that are much bigger than their context windows.

How RAG Works:

Think about being in a big library with 10,000 books. “What’s the recipe for chocolate chip cookies?” someone asks.

Without RAG (The Dumb Way):

• Take all 10,000 books to your desk.
• Try to read them all at once.
• Feel overwhelmed and lost.
• Take a long time to find the answer.

With RAG (The Smart Way):

Step 1: Organize the Library (Done Once)

• Put a label on each book that says “Cooking”, “History”, “Science”, etc.
• Make a card catalogue that works like a magic search index.
• Every card points to books that are related.

Step 2: Smart Search (Every Question)

• Question: “Chocolate chip cookie recipe?”
• The magic catalogue says, “Check the ‘Baking Cookbook’ on shelf 5!”
• Grab ONLY that one book (not all 10,000!).

Step 3: Quick Answer

• Read only the cookie section (not the whole book).
• Find the recipe.
• Give the answer.

You only had to work with one book instead of ten thousand! That’s RAG: finding the needle without having to carry the whole haystack.

The RAG Pipeline in More Detail

┌─────────────────────────────────────────────────┐
│ >INDEXING PHASE(Done Once)                      │
│                                                  │
│  1. Split documents into chunks                 │
│     "Large Document" → [Chunk1, Chunk2, ...]   │
│                                                  │
│  2. Create embeddings for each chunk            │
│     Chunk1 → [0.23, 0.45, ...] (vector)        │
│                                                  │
│  3. Store in vector database                    │
│     VectorDB.insert(chunk_id, embedding, text) │
└─────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────┐
│ RETRIEVAL PHASE (Every Query)                   │
│                                                  │
│  1. User asks question                          │
│     "How do I reset my password?"              │
│                                                  │
│  2. Create question embedding                   │
│     Question → [0.21, 0.47, ...] (vector)      │
│                                                  │
│  3. Find similar chunks                         │
│     VectorDB.similarity_search(question_vec)   │
│                                                  │
│  4. Retrieve top K most similar chunks          │
│     Returns: [Chunk45, Chunk12, Chunk89]       │
│                                                  │
│  5. Load ONLY those chunks into context         │
│                                                  │
│  6. Generate response                           │
│     AI uses just 3 chunks (not all 1000!)      │
└─────────────────────────────────────────────────┘

Advanced RAG Techniques:

Hybrid Search (Keyword + Semantic)

Think of two friends helping you look for a toy you lost:

• Friend 1 (Semantic Search): “Did you lose something round and red? I It might be in the toy box with other balls!”
  • Understands meaning and finds similar things
• Friend 2 (Keyword Search): “You said ‘red ball’? I’ll look for anything labelled ‘red’ or ‘ball’!”
  • Looks for exact words and labels
• Together: They put their results together and show you the best ones!
  • Friend 1 found three types of balls: a red ball, a red bouncy ball, and a rubber ball.
  • Friend 2 found: a red ball, a ball pit, and a red balloon.
  • Combined: red ball (both found it!), bouncy ball, rubber ball < Best results!

Using both methods together finds better answers than using just one!

Contextual Chunk Retrieval

Imagine reading a storybook, and you find the perfect sentence on page 42:

Without Context:

• You only read that one sentence.
• “…and then she opened the door.”
• Wait… who is “she”? What door? Why?

With Context (The Smart Way):

• Read the page before (page 41): “Sarah walked nervously toward the old house…”
• Read the target page (page 42): “…and then she opened the door.”
• Read the page after (page 43): “Inside, she found the mysterious box…”

Now you understand! Sarah is the character, it’s an old house door, and she’s searching for something.

The lesson is to not just take the exact piece; take a little bit before and after to get the whole picture!

Reranking for Precision

I think about what the best pizza toppings are:

First Search: Someone quickly grabs 10 random toppings from the pantry:

• Pepperoni ✓ (good!)
• Chocolate chips ✗ (weird on pizza…)
• Mushrooms ✓ (good!)
• Gummy bears ✗ (definitely not!)
• Cheese ✓ (perfect!)

Reranking (The Smart Judge): Now a pizza expert looks at these 10 items and rates them:

• Pepperoni: 9/10
• Cheese: 10/10
• Mushrooms: 8/10
• Chocolate chips: 1/10
• Gummy bears: 0/10

Final Selection: Pick the top 5 rated items!

The reranker is like a specialist who double-checks the first search and puts the best items at the top!

RAG Impact:

If done right, RAG can make a big difference in performance. Cornell University research indicates that employing RAG for tool descriptions can enhance tool selection accuracy by threefold in comparison to loading all tools into context.

The Berkeley Function-Calling Leaderboard demonstrates that every model does worse with more tools, even when they are within their context window limits.

Technique 2.2: Dynamic Context Loading

Imagine you’re packing for a day trip, and your backpack can hold 10 pounds:

Priority 1 – Critical (MUST pack)

• Water bottle (2 pounds) → Always pack this!
• Your phone (1 pound) → Must have!
• Current weight: 3 pounds

Priority 2 – Important (Pack if room)

• Lunch box (2 pounds) → Got room? Yes! Pack it.
• Sunscreen (0.5 pounds) → Got room? Yes! Pack it.
• Current weight: 5.5 pounds.

Priority 3 – Nice-to-Have (Fill remaining space)

• Comic book (1 pound) → Still have room? Yes!
• Frisbee (0.5 pounds) → Still have room? Yes!
• Skateboard (4 pounds) → Will this fit? No! (would make it 11 pounds).
• Current weight: 7 pounds.

Final backpack: Water, phone, lunch, sunscreen, comic book, frisbee = 7 pounds (under 10 limit!).

The AI does the same thing! It packs the most important information first, then adds more until the backpack (context window) is nearly full, but never overflowing.

Technique 2.3: Metadata Filtering

The Smart Pre-Filter Strategy

Imagine you’re looking for your red LEGO car in a huge toy room:

Dumb Way

• Search through ALL toys (dolls, blocks, cars, puzzles, stuffed animals…).
• Takes forever!
• Find 100 toys, most not even cars.

Smart Way (Metadata Filtering)

• Filter 1: “Only show me cars” (not dolls, not blocks).
  • Now looking at 20 cars instead of 1000 toys.
• Filter 2: “Only red ones” (not blue, not yellow).
  • Now looking at 5 red cars.
• Filter 3: “Only LEGOs” (not Hot Wheels, not wooden cars).
  • Found it! 1 red LEGO car.

Why this is brilliant:

• Faster: Searched 5 items instead of 1000.
• More accurate: Every result is relevant.
• Respects rules: Won’t show you toys that belong to your sibling.
• Fresh: Can filter to “only toys bought this month”.

The AI does this with information – it filters first, then searches!

Real-World Example: Enterprise Documentation Assistant

Let’s see how WRITE and SELECT work together in practice:

Challenge: The company has 10,000 pages of documentation. The AI assistant should answer employee questions.

Without Context Engineering

• Try to load all 10,000 pages → Context overflow.
• Load random pages → Wrong answers.
• Load recent pages → Misses critical info.

With Context Engineering (WRITE + SELECT)

WRITE Phase

Step 1: Organize all documentation
  - Create labels and categories
  - >Add metadata (department, date, topic)
  - Build search index

Step 2: Store in structured format
  - HR policies → HR database
  - IT procedures → IT database
  - Product docs → Product database

SELECT Phase

Employee asks: "What's our PTO policy?"
Step 1: Identify intent → HR question
Step 2: Filter metadata → HR database only
Step 3: RAG search → "PTO policy" sections
Step 4: Retrieve → 3 >relevant pages (not 10,000!)
Step 5: Load into context → Just those 3 pages
Step 6: Generate answer → Fast and accurate!

Result: Fast, accurate answers without context overload!

How We’re Applying WRITE and SELECT at LambdaTest?

At LambdaTest, we’re deeply invested in building intelligent AI agents. Here’s how we’re applying these first two pillars (without revealing proprietary details):

Our WRITE Strategy

Our WRITE strategy begins with a Structured Information Architecture, ensuring that every piece of data is organized for clarity and accessibility.

Structured Information Architecture

We organize information hierarchically, following best practices, as outlined in Daffodil Software Engineering Insights.

Level 1: System Policies (rarely change)

• Core functionality rules.
• Platform capabilities.
• Security requirements.

Level 2: Feature Documentation (monthly updates)

• Feature specifications.
• Integration guidelines.
• API references.

Level 3: User Context (session-based)

• Current workflow state.
• User preferences.
• Active task details.

Level 4: Interaction History (turn-based)

• Recent actions.
• Generated outputs.
• Feedback received.

Why this works: AI quickly knows where to look. Need a core rule? Check Level 1. Need user preference? Check Level 3.

Memory Management

We implement both short-term and long-term memory:

• Short-term (Session Memory): Tracks the current workflow, decisions made, and progress.
• Long-term (Cross-Session Memory): Learns user patterns, common workflows, and domain-specific knowledge.

The AI gets smarter with each interaction, like an experienced colleague!

Our SELECT Strategy

Our SELECT strategy focuses on Intelligent Context Retrieval, carefully identifying and extracting the most relevant information for each task.

Intelligent Context Retrieval

We don’t load everything at once. Instead:

1. Analyze the task → Determine what information categories are needed.
2. Fetch relevant subset → Use semantic search and metadata filtering.
3. Load minimal context → Only what’s necessary for current step.
4. Cache for reuse → If the same information is needed again.

Example Workflow:

User Task: "Process this technical document"

Step 1: Identify document type
  Context loaded: >Document type classifiers (lightweight)

Step 2: Extract key information
  Context loaded: Extraction patterns for this type

Step 3: Generate structured output
  Context loaded: Output schemas and examples

Each step: Different focused context!

Results We’re Seeing:

While we can’t share exact numbers, we’re experiencing:

• Dramatically improved accuracy across workflows.
• Significant reduction in processing time.
• Better cost efficiency per operation.
• More consistent, reliable outputs.
• Enhanced user satisfaction.

The investment in Context Engineering has fundamentally transformed our AI agents from “useful” to “production-grade””.

Key Takeaways From Part 1

Here are some key takeaways summarized from this blog:

The Four Failure Modes (What Goes Wrong)

Understanding the common failure modes in context management is crucial for reliable AI performance. Each type highlights how improperly handled information can undermine results:

• Context Poisoning → Mistakes get saved and reused.
• Context Distraction → Too much info, AI loses focus.
• Context Confusion → Irrelevant info influences decisions.
• Context Clash → Contradictory info causes inconsistency.

The First Two Solutions (What to Do)

The first two solutions focus on capturing and retrieving information effectively. WRITE ensures that knowledge is recorded systematically and organized for easy access, while SELECT emphasizes retrieving the most relevant context to guide AI decision-making.

WRITE

• Use scratchpads for temporary notes.
• Build structured long-term memory (semantic, episodic, procedural).
• Organize information hierarchically.
• Tag and timestamp everything.

SELECT

• Use RAG for large knowledge bases.
• Apply hybrid search (semantic + keyword).
• Grab context around relevant chunks.
• Rerank for precision.
• Load dynamically by priority.
• Filter by metadata first.

The Core Insight

Don’t try to load everything into the AI’s “backpack” at once. Instead:

• WRITE it down in organized notes.
• SELECT only what you need right now.

This is like the difference between:

• Carrying all your textbooks to every class.
• Bringing only today’s math book to math class.

What’s Coming in Part 2

In the Part 2 of Context Engineering series, we’ll explore:

• Pillar 3: COMPRESS – Making information smaller without losing meaning.
  • Hierarchical summarization.
  • Sliding windows for conversations.
  • Tool output compression.
  • When to use lossy vs lossless compression.
• Pillar 4: ISOLATE – Separating concerns for clarity
  • Multi-agent architectures (90.2% performance improvement!).
  • Sandboxed execution.
  • State-based isolation.
• Production Challenges – Real engineering lessons
  • Handling errors that compound.
  • Debugging non-deterministic systems.
  • Deployment strategies.
  • Anthropic’s hard-won lessons from production.
• Advanced Patterns – Taking it to the next level
  • Context tiering strategies.
  • Long-horizon conversation management.
  • Smart caching techniques.

Don’t miss Part 2!

Further Reading

These resources expand on the foundations introduced in Part 1, offering practical insights and technical depth for anyone building reliable, context-aware AI agents. Each piece adds a unique perspective on solving memory and context challenges in real-world deployments.

Essential for Understanding Part 1

These foundational resources clarify why context failures happen and how structured engineering principles resolve them. They offer practical insights into maintaining reliable, scalable AI reasoning.

• How Long Contexts Fail (and How to Fix Them): Drew Breunig’s essential guide covering the four context failure modes with extensive research citations.
• Context Engineering for AI Agents: A comprehensive guide covering the four pillars (WRITE, SELECT, COMPRESS, ISOLATE).
• Anthropic’s Multi-Agent Research System: Deep dive into building production multi-agent systems.

Research Referenced

These studies highlight real-world evidence of context degradation in advanced AI agents. They underscore the need for disciplined context management to sustain accuracy, stability, and consistent performance.

• DeepMind Gemini 2.5 Technical Report: Context poisoning in game-playing agents.
• Microsoft/Salesforce Sharded Prompts Study: 39% performance drop from context clash.
• Berkeley Function-Calling Leaderboard: Every model performs worse with more tools.
• Daffodil Software Best Practices: Hierarchical organization patterns.

Frequently Asked Questions (FAQs)

Why do AI agents forget information during a conversation?

AI agents forget because their context window has limits. Once that window fills with tokens, older information gets pushed out. The model can only process what remains visible inside that window, so earlier details effectively disappear from its working memory during generation.

Is this forgetting a design flaw in AI models?

No, it is a structural limitation rather than a defect. Language models are built to process a fixed number of tokens. When the limit is reached, older content falls off. Forgetting reflects the model’s operational boundaries, not a functional failure in design.

How do tokens influence what the AI remembers?

Tokens represent every word, symbol, and space. The model handles only a limited number per session. When you exceed that number, newer text replaces older tokens. Managing prompt length and clarity helps preserve vital context and delay the onset of forgetting.

Do larger AI models remember more information?

Yes, within limits. Larger models typically include wider context windows, allowing them to retain more tokens before truncating older content. However, no model remembers indefinitely. Once the window’s maximum token capacity is reached, earlier context is still lost regardless of size.

Can fine-tuning help prevent forgetting?

Fine-tuning strengthens pattern recognition and improves contextual understanding, but it does not expand memory capacity. The model still forgets once token limits are reached. Persistent memory requires an external storage mechanism capable of recalling information beyond the built-in context window.

Why do AI agents sometimes remember only fragments?

Partial recall occurs when bits of earlier information remain within the visible token window. The model can reference these fragments but loses everything that has scrolled beyond the limit. The result is selective memory, which creates uneven continuity in longer interactions.

How does poor prompt design increase forgetting?

Inefficient prompts waste tokens on unnecessary wording or repetition. This causes critical details to drop from the context window sooner. Concise, structured prompts preserve valuable space and ensure the model focuses on essential information needed to maintain logical conversation continuity.

Why can’t AI agents hold long-term memory like humans?

AI lacks persistent biological memory. Each session is stateless unless an external system records and reintroduces information. Without such integration, the model resets after every interaction. It recalls only what fits inside its active window, losing all prior conversational context.

How does summarization reduce memory loss?

Summarization compresses earlier text into concise, meaningful form. By reintroducing these summaries instead of full transcripts, the AI retains context without overloading tokens. This technique maintains coherence over extended conversations while keeping the total input safely within processing limits.

What is the most practical way to fix AI forgetting?

Use a combination of summarization and external memory. Summaries maintain short-term coherence, while an external database stores key facts for recall. Feeding this structured context back to the model allows consistent continuity across long interactions without exceeding the token window.




Co-Author: Sai Krishna

Sai Krishna is a Director of Engineering at LambdaTest. As an active contributor to Appium and a member of the Appium organization, he is deeply involved in the open-source community. He is passionate about innovative thinking and love to contribute to open-source technologies. Additionally, he is a blogger, community builder, mentor, international speaker, and conference organizer.

]]> https://www.lambdatest.com/blog/kaneai-is-now-live-on-product-hunt/ Wed, 15 Oct 2025 07:39:40 +0000 https://www.lambdatest.com/blog/?p=92647

At LambdaTest, we believe the future of software testing lies in harnessing the power of AI. With that vision in mind, today we are live with KaneAI on Product Hunt.

KaneAI is a GenAI-native testing agent designed to revolutionize the way teams approach test automation. By bridging the gap between natural language and executable test code, KaneAI empowers users to plan, author, and evolve end to end automation tests without requiring complex scripting or framework expertise.



Why AI in Testing Matters Now More Than Ever

The software development lifecycle is evolving at an unprecedented pace, and so are the demands on testing teams. With the increasing complexity of applications, manual testing is no longer sufficient to ensure quality at speed. GenAI-native testing solutions like KaneAI are crucial to speeding up the testing process while maintaining high-quality standards.



According to industry reports, over 80% of organizations are now adopting automation tools to handle their testing requirements. However, these tools often require deep technical knowledge and scripting skills, which is where KaneAI comes in. It lowers the barrier to automation by enabling non-technical team members to contribute to the testing process, making AI-driven testing accessible to all.

Introducing KaneAI: Revolutionizing Test Automation

KaneAI is now live on Product Hunt, ready to help you streamline your testing workflows and drive efficiency. Whether you’re testing web, mobile, or both, KaneAI can handle it all with ease. Here’s what makes KaneAI stand out:

• Natural Language Test Authoring: Simply converse in natural language, and KaneAI will generate test cases for you. No more manual scripting or writing complex code!
• Multi-modal Test Generation Approach: Transform diverse input formats like text, JIRA tickets, PRDs, images, audio, videos, spreadsheets, and more into structured test cases.
• Smart Test Versioning: Keep track of test versions intelligently and ensure your tests evolve alongside your project’s development.
• API Testing: Easily integrate API testing into your automation process for a more comprehensive test suite.
• Cross-Platform Testing: Test seamlessly across web and mobile platforms, ensuring complete coverage and more..

Give It a Spin!

Visit Product Hunt to try out KaneAI, explore its full potential, and share your thoughts with us. We look forward to hearing your feedback as we continue to evolve KaneAI to meet the needs of modern testing teams.

]]> https://www.lambdatest.com/blog/pytest-code-coverage-report/ Tue, 14 Oct 2025 08:40:05 +0000 https://www.lambdatest.com/blog/?p=43043

This article is a part of our Content Hub. For more in-depth resources, check out our content hub on pytest Tutorial.

In Python development, creating automated tests is only one part of ensuring high-quality software. Equally important is understanding how much of your code is actually covered by tests.

pytest coverage provides insights into untested code, helping developers ensure that critical paths are thoroughly checked.

In this guide, we’ll dive into pytest code coverage, and show how to generate detailed pytest coverage reports.

Overview

pytest coverage refers to the percentage of your Python code that is executed while running automated tests.

Why is pytest coverage reports important?

Pytest coverage reports are important because they highlight which parts of your python code are tested, uncover gaps in your test suite, and help ensure reliable, high-quality software.

How do pytest-cov and coverage.py tools help in pytest coverage reports?

pytest-cov is a pytest plugin for measuring test coverage, while coverage.py is the underlying Python tool that tracks code execution and generates detailed coverage reports.

How to Generate pytest Code Coverage Report?

You can generate a pytest code coverage report by installing pytest-cov and running:

pytest --cov=your_module tests/ --cov-report=html

TABLE OF CONTENTS

• What is pytest Coverage?
• What is the pytest Coverage Report?
• Why pytest for Code Coverage Reports?
• pytest Code Coverage Reporting Tools
• Demo: How to Generate pytest Code Coverage Report?
• Frequently Asked Questions (FAQs)

What is pytest Coverage?

pytest coverage refers to the practice of measuring how much of your Python code is exercised by automated tests written with pytest.

pytest code coverage is a key metric in software testing because it helps identify untested parts of your codebase, reduces bugs, and ensures robust software.

High coverage doesn’t guarantee bug-free software, but it highlights areas that need more attention, reducing the risk of hidden issues.

When you integrate coverage analysis with pytest using pytest-cov, you can:

• Determine which lines of code are executed during tests.
• Identify gaps in your test suite.
• Generate detailed pytest coverage reports in multiple formats (terminal, HTML, XML, etc.).

Example:

pytest --cov=your_module tests/ --cov-report=html

What is the pytest Coverage Report?

A pytest coverage report shows which parts of your Python code are executed during tests. It highlights tested and untested lines, measures overall coverage percentages, and tracks branch coverage.

These reports help developers identify gaps in their test suites, improve test effectiveness.

You can generate coverage reports in multiple formats, such as terminal, HTML, or XML, making it easy to review coverage locally or integrate it into CI/CD pipelines.

Why pytest for Code Coverage Reports?

pytest has plugins and supported modules for evaluating code coverage. Here are some reasons you want to use pytest for code coverage report generation:

• It provides a straightforward approach for calculating coverage with a few lines of code.
• It gives comprehensive statistics of your code coverage score.
• It features plugins that can help you prettify pytest code coverage reports.
• It features a command-line utility for executing code coverage.
• It supports distributed and localized testing.
Note

Get 100 Minutes of Automated Testing for FREE. Try LambdaTest Today!

pytest Code Coverage Reporting Tools

Using tools like pytest-cov and coverage.py, teams can generate comprehensive pytest coverage reports, analyze which parts of the codebase need additional testing, and track coverage trends over time.

Here are some of the most used pytest code coverage tools.

pytest-cov

pytest-cov is a code coverage plugin and command line utility for pytest. It also provides extended support for coverage.py.

Like coverage.py, you can use pytest-cov to generate HTML or XML reports in pytest and view a pretty code coverage analysis via the browser. Although using pytest-cov involves running a simple command via the terminal, the terminal command becomes longer and more complex as you add more coverage options.
For instance, generating a command-line-only report is as simple as running the following command:

pytest --cov

The result of the pytest –cov command is shown below:



But generating an HTML report requires an additional command:

pytest --cov --cov-report=html:coverage_re

Where coverage_re is the coverage report directory. Below is the report when viewed via the browser:



Here is a list of widely-used command line options with –cov:

–cov options	Description
-cov=PATH	Measure coverage for a filesystem path. (multi-allowed)
–cov-report=type	To specify the type of report to generate. Specify the type of report to generate. Type can be HTML, XML, annotate, term, term-missing, or lcov.
–cov-config=path	Config file for coverage. Default: .coveragerc
–no-cov-on-fail	Do not report coverage if the test fails. Default: False
–no-cov	Disable coverage report completely (useful for debuggers). Default: False
–cov-reset	Reset cov sources accumulated in options so far. Mostly useful for scripts and configuration files.
–cov-fail-under=MIN	Fail if the total coverage is less than MIN.
–cov-append	Do not delete coverage but append to current. Default: False
–cov-branch	Enable branch coverage.
–cov-context	Choose the method for setting the dynamic context.

coverage.py

coverage.py

The coverage.py library is one of the most-used pytest code coverage reporting tools. It’s a simple Python tool for producing comprehensive pytest code coverage reports in table format. You can use it as a command-line utility or plug it into your test script as an API to generate coverage analysis.

The API option is recommended if you need to prevent repeating a bunch of terminal commands each time you want to run the coverage analysis.

While its command line utility might require a few patches to prevent reports from getting muffed, the API option provides clean, pre-styled HTML reports you can view via a web browser.

Below is the command for executing code coverage with pytest using coverag.py:

coverage run -m pytest

The above command runs all pytest test suites with names starting with “test.”
All you need to do to generate reports while using its API is to specify a destination folder in your test code. It then overwrites the folder’s HTML report in subsequent tests.

Explore the pytest-cov GitHub repository to access the official plugin for creating detailed pytest code coverage reports, complete with setup guides, examples, and configuration tips to boost your Python testing workflow.

Subscribe to the LambdaTest YouTube Channel and stay updated with the latest video tutorials on Selenium testing, Selenium Python, and more!

Demo: How to Generate pytest Code Coverage Report?

Generating a coverage report provides both a summary and detailed insights, helping improve overall code quality and test effectiveness.

The demonstration for generating pytest code coverage includes a test for the following:

• A plain name tweaker class example to show why you may not achieve 100% code coverage and how you can use its result to extend your test reach.
• Code coverage demonstration for registration steps using the LambdaTest eCommerce Playground, executed on the cloud grid.

We’ll use Python’s coverage module, coverage.py, to demonstrate the code coverage for all the tests in this tutorial on the pytest code coverage report. So you need to install the coverage.py module since it’s third-party. You’ll also need to install the Selenium WebDriver (to access WebElements) and python-dotenv (to mask your secret keys).

If you are new to Selenium WebDriver, check out our guide on what is Selenium WebDriver.

Create a requirements.txt file in your project root directory and insert the following packages:

Filename – requirements.txt

coverage
selenium
python-dotenv
pytest

Next, install the packages using pip:

pip install -r requirements.txt

As mentioned, coverage.py lets you generate and write coverage reports inside an HTML file and view it in the browser. You’ll see how to do this later.

Name Tweaker Class Test

We’ll start by considering an example test for the name tweaking class to demonstrate why you may not achieve 100% code coverage. And you’ll also see how to extend your code coverage.

The name tweaking class contains two methods. One is for concatenating a new and an old name, while the other is for changing an existing name.

Filename: plain_tests/plain_tests.py

class test_should_tweak_name:
    def __init__(self, name) -> None:
        self.name = name
           
    def test_should_addNames(self, name):
        if self.name == "LambdaTest":
            new_name = self.name+" "+name
            assert new_name == "LambdaTest Grid", "new_name should be LambdaTest Grid"
            return new_name
        else:
            return self.name

    def test_should_changeName(self, name):
        self.name = name
        assert self.name == "LambdaTest Cloud Grid", "new_name should be LambdaTest Cloud Grid"
        return name

To execute the test and get code coverage of less than 100%, we’ll start by omitting a test case for the else statement in the first method and ignore the second method entirely (test_should_changeName).

Filename: run_coverage/name_tweak_coverage.py

# Import the Pytest coverage plugin:
import coverage

# Start code coverage before importing other modules:
cov = coverage.Coverage()
cov.start()

# Main code to be covered----------:

import sys
sys.path.append(sys.path[0] + "/..")

from plain_tests.plain_tests import test_should_tweak_name

tweak_names = test_should_tweak_name("LambdaTest")
print(tweak_names.test_should_addNames("Grid"))

cov.stop()
cov.save()
cov.html_report(directory='coverage_reports')

Run the test by running the following command:

run_coverage/name_tweak_coverage.py

Go into the coverage_reports folder and run index.html via your browser. The test yields 69% coverage (as shown below) since it omits the two named instances.



Let’s extend the code coverage.

Although we deliberately ignored the second method in that class, it was easy to forget to include a case for the else statement in the test. That’s because we only focused on validating the true condition. Including a test case that assumes negativity (where the condition returns false) extends the code coverage.

So what if we add a test case for the second method and another one that assumes that the provided name in the first method isn’t LambdaTest?

The code coverage yields 100% since we’re considering all possible scenarios for the class under test.

So, a more inclusive test looks like this:

Filename: run_coverage/name_tweak_coverage.py

# Import the Pytest coverage plugin:
import coverage

# Start code coverage before importing other modules:
cov = coverage.Coverage()
cov.start()

# Main code to be covered----------:

import sys
sys.path.append(sys.path[0] + "/..")

from plain_tests.plain_tests import test_should_tweak_name


tweak_names = test_should_tweak_name("LambdaTest")

will_not_tweak_names = test_should_tweak_name("Not LambdaTest")

print(tweak_names.test_should_addNames("Grid"))

print(tweak_names.test_should_changeName("LambdaTest Cloud Grid"))

print(will_not_tweak_names.test_should_addNames("Grid"))

   
# Stop code coverage and save the output in a reports directory---------:
cov.stop()
cov.save()
cov.html_report(directory='coverage_reports')

Adding the will_not_tweak_names variable covers the else condition in the test. Additionally, calling test_should_changeName from the class instance captures the second method in that class.



Extending the coverage this way generates 100% code coverage, as seen below:



Code Coverage on the Cloud Grid

We’ll use the previous code structure to implement the code coverage on the cloud grid. Here, we’ll write test cases for the registration actions on the LambdaTest eCommerce Playground. Then, we’ll perform pytest testing on cloud-based testing platforms like LambdaTest.

Tests will be run based on the registration actions without providing some parameters. This might involve failure to fill in some form fields or submitting an invalid email address.

LambdaTest is an AI-native test execution platform that enables you to conduct Python web automation on a dependable and scalable online Selenium Grid infrastructure, spanning over 3000 real web browsers and operating systems.

Test Scenario 1:

Submit the registration form with an invalid email address and missing fields.

Test Scenario 2:

Submit the form with all fields filled appropriately (successful registration).

We’ll also see how adding the missing parameters can extend the code coverage. Here is our Selenium automation script:

Filename: setup/setup.py

from selenium import webdriver

from dotenv import load_dotenv
import os
load_dotenv('.env')

LT_GRID_USERNAME = os.getenv("LT_GRID_USERNAME")
LT_ACCESS_KEY = os.getenv("LT_ACCESS_KEY")

desired_caps = {
        'LT:Options' : {
            "user" : os.getenv("LT_GRID_USERNAME"),
            "accessKey" : os.getenv("LT_ACCESS_KEY"),
            "build" : "Test Coverage Idowu",
            "name" : "Firefox coverage demo2",
            "platformName" : os.getenv("TEST_OS")
        },
        "browserName" : "FireFox",
        "browserVersion" : "125.0",
    }
gridURL = "https://{}:{}@hub.lambdatest.com/wd/hub".format(LT_GRID_USERNAME, LT_ACCESS_KEY)

class testSettings:
       
    def __init__(self) -> None:
        self.driver = webdriver.Remote(command_executor=gridURL, desired_capabilities= desired_caps)
       
    def testSetup(self):
        self.driver.implicitly_wait(10)
        self.driver.maximize_window()
    def tearDown(self):
        if (self.driver != None):
            print("Cleaning the test environment")
            self.driver.quit()



Code Walkthrough:

First, import the Selenium WebDriver to configure the test driver. Get your grid username and access key (passed as LT_GRID_USERNAME and LT_GRID_ACCESS_KEY, respectively) from Settings > Account Settings > Password & Security.



The desired_caps is a dictionary of the desired capabilities for the test suite. It details your username, access key, browser name, version, build name, and platform type that runs your driver.



Next is the gridURL. We access this using the access key and username declared earlier. We then pass this URL and the desired capability into the driver attribute inside the __init__ function.

Write a testSetup() method that initiates the test suite. It uses the implicitly_wait() function to pause for the DOM to load elements. It then uses the maximize_window() method to expand the chosen browser frame.

However, the tearDown() method helps stop the test instance and closes the browser using the quit() method.

Filename: locators/locators.py

from selenium.webdriver.common.by import By
from selenium.common.exceptions import NoSuchElementException

class element_locator:
    first_name = "//input[@id='input-firstname']"
    last_name = "//input[@id='input-lastname']"
    email = "//input[@id='input-email']"
    telephone = "//input[@id='input-telephone']"
    password = "//input[@id='input-password']"
    confirm_password = "//input[@id='input-confirm']"
    subscribe_no = "//label[@for='input-newsletter-no']"
    agree_terms = "//label[@for='input-agree']"
    submit = "//input[@value='Continue']"
    error_message = "//div[@class='text-danger']"

locator = element_locator()

class registerUser:
    def __init__(self, driver) -> None:
        self.driver=driver
    def error_message(self):  
        try:
            return self.driver.find_element(By.XPATH, locator.error_message).is_displayed()
        except NoSuchElementException:
            print("All code in registration test covered")

    def test_getWeb(self, URL):
        self.driver.get(URL)
       
    def test_getTitle(self):
        return self.driver.title
    def test_fillFirstName(self, data):
        self.driver.find_element(By.XPATH, locator.first_name).send_keys(data)
    def test_fillLastName(self, data):
        self.driver.find_element(By.XPATH, locator.last_name).send_keys(data)
    def test_fillEmail(self, data):
        self.driver.find_element(By.XPATH, locator.email).send_keys(data)
    def test_fillPhone(self, data):
        self.driver.find_element(By.XPATH, locator.telephone).send_keys(data)
    def test_fillPassword(self, data):
        self.driver.find_element(By.XPATH, locator.password).send_keys(data)
    def test_fillConfirmPassword(self, data):
        self.driver.find_element(By.XPATH, locator.confirm_password).send_keys(data)
    def test_subscribeNo(self):
        self.driver.find_element(By.XPATH, locator.subscribe_no).click()
    def test_agreeToTerms(self):
        self.driver.find_element(By.XPATH, locator.agree_terms).click()
    def test_submit(self):
        self.driver.find_element(By.XPATH, locator.submit).click()

Start by importing the Selenium By object into the file to declare the locator pattern for the DOM. We’ll use the NoSuchElementException to check for an error message in the DOM (in case of invalid inputs).

Next, declare a class to hold the WebElements. Then, create another class to handle the web actions for the registration form.

The element_selector class contains the WebElement locations. Each uses the XPath locator.



The registerUser class accepts the driver attribute to initiate web actions. You’ll get the driver attribute from the setup class while instantiating the registerUser class.

The error_message inside the registerUser class does two things. First, it checks for invalid field error messages in the DOM when the test tries to submit the registration form with unacceptable inputs. The check runs every time inside a try block. So, the test covers it regardless.

Secondly, it runs the code in the try block if it finds an input error message element in the DOM. This prevents the except block from running, flagging it as non-covered code.

Otherwise, Selenium raises a NoSuchElementException. This forces the test to log the print in the except block and mark it as covered code. This feels like a reverse strategy. But it helps code coverage capture more scenarios.



So, besides capturing omitted fields (web action methods not included in the test execution), it ensures that the test accounts for an invalid email address or empty string input.

Thus, if the error message is displayed in the DOM, the method returns the error message element. Otherwise, Selenium raises a NoSuchElementException, forcing the test to log the printed message.

The rest of the class methods are web action declarations for the locators in the element_locator class. Excluding the fields that require a click action, the other class methods accept a data parameter, a string that goes into the input fields.

But first, create a test runner file for the code coverage scenarios. You’ll execute this file to run the test and calculate the code coverage.

Filename: run_coverage/run_coverage.py

# Import the Pytest coverage plugin:
import coverage

# Start code coverage before importing other modules:
cov = coverage.Coverage()
cov.start()

# Main code to be covered----------:

import sys
sys.path.append(sys.path[0] + "/..")
from testscenario.scenarioRun import test_registration

registration = test_registration()
registration.it_should_register_user()
   
# Stop code coverage and save the output in a reports directory---------:
cov.stop()
cov.save()
cov.html_report(directory='coverage_reports')

The above code starts by importing the coverage module. Next, declare an instance of the coverage class and call the start() method at the top of the code. Once code coverage begins, import the test_registration class from scenarioRun.py. Instantiate the class as registration.

The class method, it_should_register_user, is a test method that executes the test case (you’ll see this class in the next section). Use cov.stop() to close the code coverage process. Then, use cov.save() to capture the coverage report.

The cov.html_report() method writes the coverage results into an HTML file inside the declared directory (coverage_reports).

And running this file executes the test and coverage report.

Now, let’s tweak the web action methods inside scenarioRun.py to see the difference in code coverage for each scenario.

Test Scenario 1: Submit the registration form with an invalid email address and some missing fields.

Filename: testscenario/scenarioRun.py

import sys
sys.path.append(sys.path[0] + "/..")

from locators.locator import registerUser
from setup.setup import testSettings
import unittest
from dotenv import load_dotenv
import os

load_dotenv('.env')

setup = testSettings()

test_register = registerUser(setup.driver)

E_Commerce_palygroud_URL = "https:"+os.getenv("E_Commerce_palygroud_URL")

class test_registration(unittest.TestCase):

    def it_should_register_user(self):
        setup.testSetup()
        test_register.test_getWeb(E_Commerce_palygroud_URL)
        title = test_register.test_getTitle()

        self.assertIn("Register", title, "Register is not in title")

        test_register.test_fillEmail("testrs@gmail")
       
        test_register.test_fillPhone("090776632")
        test_register.test_fillPassword("12345678")
        test_register.test_fillConfirmPassword("12345678")
       
        test_register.test_submit()

        test_register.error_message()
       
        setup.tearDown()

Pay attention to the imported built-in and third-party modules. We start by importing the registerUser and testSettings classes we wrote earlier. The testSettings class contains the testSetup() and tearDown() methods for setting up and closing the test, respectively. We instantiate this class as a setup.



As seen below, the registerUser class instantiates as test_register using the setup.driver attribute. The dotenv package lets you get the test website’s URL from the environment variable.

The testSetup() method initiates the test case (it_should_registerUser method) and prepares the test environment. Next, we launch the website using the test_getWeb() method. This accepts the website URL declared earlier. The inherited property from the unittest test, assertIn, checks whether the declared string is in the title. Use the setup.tearDown() method to close the browser and clean the test environment.

As earlier stated, the rest of the test case omits some methods from the registerUser class to see its effect on code coverage.



Test Execution:

To execute the test and code coverage, go into the test_run_coverage folder and run the run_coverage.py file using pytest:

pytest

Once the code runs successfully, open the coverage_reports folder and open the index.html file via a browser. The code coverage reads 94%, as shown below.





Although the other test files read 100%, locator.py covers 89% of its code, reducing the overall score to 94%. We omitted some web actions and entered an invalid email address while running the test.

Opening locator.py gives more insights into the missing steps (highlighted in red), as shown below.



Although you might expect the coverage to flag the test_fillEmail() method, it doesn’t because the test provided an email address; it was only invalid. The except block is the invalid parameter indicator. And it only runs if the input error message element isn’t in the DOM.

As seen, the test flags the except block this time since the input error message appears in the DOM due to invalid entries.

The test suite runs on the cloud grid with some red flags in the test video, as shown below.

Test Scenario 2: Submit the form with all fields filled appropriately (successful registration).

import sys
sys.path.append(sys.path[0] + "/..")

from locators.locator import registerUser
from setup.setup import testSettings
import unittest
from dotenv import load_dotenv
import os

load_dotenv('.env')

setup = testSettings()

test_register = registerUser(setup.driver)

E_Commerce_palygroud_URL = "https:"+os.getenv("E_Commerce_palygroud_URL")

class test_registration(unittest.TestCase):
   

    def it_should_register_user(self):
       
        setup.testSetup()
        test_register.test_getWeb(E_Commerce_palygroud_URL)
        title = test_register.test_getTitle()

        self.assertIn("Register", title, "Register is not in title")
       
        test_register.test_fillFirstName("Idowu")
        test_register.test_fillLastName("Omisola")

        test_register.test_fillEmail("testrs@gmail.com")
       
        test_register.test_fillPhone("090776632")
        test_register.test_fillPassword("12345678")
        test_register.test_fillConfirmPassword("12345678")
        test_register.test_subscribeNo()
        test_register.test_agreeToTerms()
        test_register.test_submit()

        test_register.error_message()
       
        setup.tearDown()

Test Scenario 2 has a code structure and naming convention similar to Test Scenario 1. However, we’ve expanded the test reach to cover all test steps in Test Scenario 2. Import the needed modules as in the previous scenario. Then instantiate the testSettings and registerUser classes as setup and test_register, respectively.

To get an inclusive test suit, ensure that you execute all the test steps from the registerUser class, as shown below. We expect this to generate 100% code coverage.

Test Execution:

Go into the run_coverage folder and run the pytest command to execute the test_run_coverage.py file:

pytest

Open the index.html file inside the coverage reports via a browser to see your pytest code coverage report. It’s now 100%, as shown below. This means the test doesn’t omit any web action.



Below is the test suite execution on the cloud grid:



Conclusion

Manually auditing your test suite can be an uphill battle, especially if your application code base is large. While performing Selenium Python testing, leveraging a dedicated code coverage tool boosts your productivity, as it helps you flag untested code parts to detect potential bugs easily. Although you’ll still need human inputs to decide your test requirements and coverage, performing a code coverage analysis gives you clear direction.

Frequently Asked Questions (FAQs)

What is code coverage in pytest?

Code coverage in pytest measures the amount of Python code executed while the tests run. It helps identify which parts of your codebase have not been tested and thus might contain hidden bugs.

How to get Python code coverage?

To get code coverage in Python, you can use the pytest-cov plugin. Install it via pip (pip install pytest-cov), and then run your tests with pytest –cov=your_package_name to generate a coverage report.

How to increase test coverage in pytest?

To increase test coverage in pytest:

• Identify untested code parts using coverage reports.
• Write additional tests for those parts, focusing on edge cases and error handling.
• Continuously review and refactor tests to improve coverage metrics and test suite quality.

How to run pytest with coverage?

Install the coverage plugin and run your tests with coverage enabled to measure how much of your code is tested.

How to run pytest coverage?

Use the coverage option in pytest to execute tests while tracking which lines of code are executed.

How to get coverage report in pytest?

Generate a coverage report in formats like terminal summary, HTML, or XML to see which parts of your code were tested.

How to check pytest coverage?

Run tests with coverage tracking and review the report to check which lines and files were covered.

How to use pytest coverage?

Enable the coverage plugin during test runs and view reports to understand test effectiveness and gaps.

How to read pytest coverage report?

Check the summary for overall coverage percentage and review detailed reports to identify untested areas.

]]> https://www.lambdatest.com/blog/qa-certifications/ Tue, 07 Oct 2025 04:00:44 +0000 https://www.lambdatest.com/blog/?p=65543

Quality Assurance (QA) ensures software meets specific quality standards through proactive processes. Organizations leverage both manual and automation testing for broader coverage, faster feedback, and reduced costs.

According to Fortunе Businеss Insights, the global automation tеsting markеt is еxpеctеd to grow from $15.39 billion in 2023 to $51.26 billion by 2030 with a CAGR of 18.8%.

The global automation testing market is expected to grow significantly, highlighting the increasing demand for skilled QA testers in businesses dealing with digital products. Obtaining a quality assurance certification is one way to demonstrate expertise in this crucial field.

Overview

A Quality Assurance certification validates your expertise in testing methodologies, tools, and best practices that ensure software quality and reliability. Whether you’re a beginner or a professional, earning a QA certification helps you stand out in the fast-growing automation testing industry.

Top Quality Assurance Certifications to Pursue in 2025

Popular QA certifications include:

• LambdaTest Certifications (Selenium, Playwright, Cypress, Appium)
• Certified Software Testing Engineer (CSTE)
• Certified Tester Advanced Level – Test Automation Engineering (CTAL-TAE)
• Certified Cloud Tester (Foundational Level)
• Certified Software Quality Analyst (CSQA)
• Automation Test Engineer by Simplilearn
• Performance Testing Using JMeter (Edureka)
• AWS Certified DevOps Engineer – Professional

Why Do You Need QA Certifications?

QA certifications enhance your credibility, expand your career opportunities, and keep your testing skills aligned with evolving automation technologies.

TABLE OF CONTENTS

• Why Do You Nееd QA Cеrtifications?
• Top Quality Assurance Certifications in 2025
• LambdaTest Certifications
• Certified Software Testing Engineer (CSTE)
• Certified Tester Advanced Level Test Automation Engineering (CTAL-TAE)
• Certified Cloud Tester (Foundational Level)
• Test Automation University
• Certified Software Quality Analyst (CSQA)
• Certified Tester Mobile Application Testing (CT-MAT)
• Certified Software Test Automation Specialist (CSTAS)
• AWS Cеrtifiеd DеvOps Enginееr – Profеssional
• Manual Software Testing: Complete Course with Practical Labs
• UI Automation and Selectors
• Automation Test Engineer
• Cеrtifiеd Jеnkins Enginееr (CJE)
• Responsive Web Design
• Performance Testing Using JMeter
• Rest API Testing (Automation) from Scratch
• Why Do Organizations Onboard Certifed QA Testers?
• Who Should Take Quality Assurance Certifications?
• What are Some Skills QA Testers Should Have?
• Time to Upskill
• Frequently Asked Questions (FAQs)

Why Do You Nееd QA Cеrtifications?

QA certifications validate your skills, boost credibility, and enhance career growth. They increase hiring opportunities, build confidence, and open paths from manual testing to advanced QA roles.

Let’s look at the following detailed benefits of getting certified in quality assurance:

• Enhance your marketability during the hiring process, as hiring managers strongly prefer candidates with training and certification in QA.
• Build confidence and a sense of accomplishment by completing online QA assessments and obtaining certifications. This will empower you to pursue various job opportunities or undertake part-time projects.
• Advance your career trajectory as QA certifications open doors to broader career profiles. You can transition from being a manual tester to a certified QA automation tester lead or expand your role from a developer to a test analyst or manager, depending on the requirements of the organization you are applying to.

Top Quality Assurance Certifications in 2025

Explore the top QA certifications in 2025 that boost your skills, credibility, and career opportunities, from beginner-friendly to advanced automation roles.

1. LambdaTest Certifications

LambdaTest is an AI-native test orchestration and execution platform that lets developers and testers perform manual and automated testing across 10,000+ real desktop and mobile environments. Besides, it also offers some of the best quality assurance certification related to automation and manual testing. For desktop browser automation, LambdaTest offers framework-based QA certifications on Selenium, Playwright, and Cypress.



With LambdaTest Certifications, you can validate and polish your test automation skills and stay ahead of your web and mobile testing game.

Let’s look at some of the QA certifications offered by LambdaTest for Selenium, Playwright, and Cypress.

Selenium certifications:

• Selenium 101
• Selenium Advanced
• Selenium Java 101
• TestNG
• JUnit
• Selenium Python 101
• Selenium JavaScript 101
• Selenium C# 101
• Selenium Ruby 101

Playwright certifications:

• Playwright 101
• Playwright 102

Cypress certifications:

• Cypress 101

Next, for mobile automation testing, LambdaTest provides Appium 101 certification that lets you brush up on your app automation skills on a real device cloud.

You can even go ahead and take LambdaTest product-based certification (also known as Bootcamp certifications). These Quality Assurance Certifications will help you elevate your manual and automation skills on cloud testing platforms like LambdaTest.



Here are the three QA certifications offered by LambdaTest Bootcamp.

• Manual Testing certification
• Automation Testing certification
• HyperExecute certification

Exam Overview:

• Registration: Visit the official LambdaTest Certifications page to register.
• Exam Duration: Objective – 45 minutes, Subjective – 36 hours (except Selenium Advanced certification, which is 48 hours.)
• Passing Marks: 70% (Cumulative)
• Cost: Free
• Validity: 2 years

For LambdaTest product-based certifications, passing criteria and duration may differ. We recommend you check the dedicated page of the respective certification for more details.

Bonus: LambdaTest LinkedIn Certifications

Here are some Quality Assurance Certifications on LinkedIn offered by LambdaTest:

• LambdaTest Test Automation Professional Certificate
• LambdaTest Software Testing Professional Certificate

2. Certified Software Testing Engineer (CSTE)

Cеrtifiеd Softwarе Tеsting Enginееr cеrtification also known as CSTE, is offered by thе Global Association of Quality Management (GAQM) to validate your proficiency in softwarе quality control techniques and principlеs.



Oncе you grab this cеrtification at thе basic lеvеl, it opеns up opportunities for you to stеp into rolеs likе a softwarе tеstеr, quality control еnginееr, quality control advisor, or managеr. This QA cеrtification plays a crucial role in prеparing you for thе rеsponsibilitiеs and tasks that comе with bеing a QA softwarе tеstеr.

Exam Overview:

• Registration: The Certified Software Testing Engineer certification requires completing an eCourse from the GAQMBok portal, as most of the questions in the test are pulled from the eCourse.
• E-Course Duration: 30 to 35 Hours
• Exam Duration: 90 minutes
• Passing Marks: 80% (64 out of 80 correct)
• Cost: Ranges from USD 150 to 270 and includes a preparation book for the exam.
• Validity: Lifetime

3. Certified Tester Advanced Level Test Automation Engineering (CTAL-TAE)

Thе Certified Tester Advanced Level Test Automation Engineering (CTAL-TAE) cеrtification from ISTQB validatеs your skills and knowledge in dеsigning, dеvеloping, and maintaining automatеd tеst solutions. So, anyone working in thе QA field can dеmonstratе their ability to automatе tеsting procеssеs еffеctivеly and еfficiеntly.



The CTAL-TAE v2.0 certification is based on the ISTQB syllabus, covering a wide range of topics, including:

• Tеst automation concеpts and mеthodologiеs.
• Test automation architecture, design, and development.
• Tеst automation dеsign and dеvеlopmеnt.
• Tеst automation еxеcution and maintеnancе.
• Intеgration of tеst automation with tеsting procеssеs.
• Test automation reporting, metrics, and continuous improvement.

Exam Overview:

• Rеgistration: You can’t rеgistеr directly with ISTQB. Instead, you nееd to rеgistеr with an accrеditеd еxam providеr.
• Exam Duration: The standard еxam duration is 90 minutes. Somе providеrs may offеr additional timе for non-nativе spеakеrs.
• Passing Marks: You nееd to scorе at lеast 65% on thе еxam to pass. Some providеrs can havе slightly different scoring systеms, so again, confirm with your providеr.
• Cost: The exam fees vary depending on the provider and your location. Generally, costs start from USD 249, but they can differ by region, and discounted retakes are sometimes available.
• Validity: Thе ISTQB CTAL-TAE cеrtification is valid for life.

4. Certified Cloud Tester (Foundational Level)

Thе Cеrtifiеd Cloud Tеstеr (Foundational Lеvеl) cеrtification assеssеs your fundamеntal knowledge and skills in tеsting cloud-basеd software applications (or services).



This QA certification aims to cover the following aspects:

• Cloud characteristics and dеploymеnt modеls.
• Cloud tеsting principlеs and mеthodologiеs.
• Sеcurity tеsting in thе cloud еnvironmеnt.
• Pеrformancе tеsting for cloud applications.
• Tools and framеworks for cloud tеsting.
• Intеgration of cloud tеsting with DеvOps practices.

Exam Overview:

• Rеgistration: You cannot rеgistеr directly on the Cеrtifiеd Cloud Tеstеr certification portal. Diffеrеnt accrеditеd organizations offеr this cеrtification, each has its rеgistration portal and procеss. You can visit thеir wеbsitе for specific instructions.
• E-Course Duration: 10 to 15 Hours
• Exam Duration: 90 minutes.
• Passing Marks: 80% (32 out of 40 correct).
• Cost: Variеs bеtwееn organizations and locations.
• Validity: Lifetime

5. Test Automation University

Tеst Automation University, also known as TAU, is an onlinе lеarning platform that focuses on tеst automation еducation and training. It providеs rеsourcеs for individuals at all lеvеls of еxpеrtisе, from thosе nеw to tеst automation to sеasonеd profеssionals sееking to advancе thеir skills.



• Usеrs can crеatе frее accounts to accеss basic contеnt and lеarning paths.
• There are also paid subscriptions to unlock additional fеaturеs and coursеs.
• TAU offers courses on various topics, including tools and framеworks (Sеlеnium, Appium, Cyprеss), mеthodologiеs (BDD, TDD), dеsign principlеs, intеgration with CI/CD pipеlinеs, and morе.

Tеst Automation Univеrsity offers various courses and lеarning paths, еach having diffеrеnt prеrеquisitеs and assеssmеnts. To gеt thе spеcific information, you nееd to idеntify thе particular TAU course or lеarning path you’rе intеrеstеd in.

Exam Overview:

• Rеgistration: You can register for the particular course from the official TAU website.
• Exam Duration: This duration will depend on the specific learning path or courses you are interested in.
• Passing Marks: TAU exams can vary in format and scoring methods. Somе may havе a minimum pеrcеntagе scorе rеquirеmеnt.
• Cost: Free, but some require a premium subscription.
• Validity: Lifetime

6. Certified Software Quality Analyst (CSQA)

Cеrtifiеd Softwarе Quality Analyst (CSQA) cеrtification hеlps dеmonstratе your professional compеtеncе in thе principlеs and practicеs of softwarе quality assurancе (SQA), Several organizations offer this software quality assurance certification, each with its own nuances and specific details



This QA certification builds upon thе CSTE foundation, covering both quality control and quality assurancе concеpts in dеpth. So, it idеal for еxpеriеncеd profеssionals sееking to polish their QA skills.

Exam Overview:

• Rеgistration: You cannot rеgistеr directly on the Cеrtifiеd Softwarе Quality Analyst certification portal. Diffеrеnt organizations offеr this cеrtification, each has its rеgistration portal and procеss. You can visit thеir wеbsitе for specific instructions.
• E-Course Duration: 20 to 25 Hours.
• Exam Duration: 90 minutes.
• Passing Marks: 80% (80 out of 100 correct).
• Cost: USD 107-250 but Variеs bеtwееn organizations and locations.
• Validity: Lifetime

7. Certified Tester Mobile Application Testing (CT-MAT)

Certified Tester Mobile Application Testing (CT-MAT) is for professionals involved in mobilе dеvicе testing and mobile application testing. This cеrtification dеmonstratеs your proficiеncy in mobilе dеvicе and mobilе app tеsting mеthodologiеs, tеchniquеs, and tools.


Every stakeholder in mobile app and device testing should take this QA certification. This includes testers of all levels, test engineers, test analysts, test managers, and software developers.

To take this exam, you need to get ISTQB Foundation Level certification.

Exam Overview:

• Rеgistration: Diffеrеnt organizations provide this cеrtification. You can visit thеir wеbsitе for specific instructions and register yourself for the ISTQB Mobilе Application Tеsting certification.
• Exam Duration: 60 minutes (extended time is available for non-English speakers).
• Passing Marks: 65% (26 out of 40 correct)
• Cost: Variеs bеtwееn organizations and locations.
• Validity: Lifetime
Note

Get certified with Appium 101. Take LambdaTest Certification Today!

8. Certified Software Test Automation Specialist (CSTAS)

Certified Software Test Automation Specialist (CSTAS) cеrtification, offered by Intеrnational Institutе for Softwarе Tеsting (IIST), is thеrе to hеlp folks in tеst automation dеvеlop thе skills thеy nееd for handling all sorts of activities rеlatеd to tеst automation.



This QA cеrtification goеs beyond just functional tеsting and includеs othеr important arеas likе pеrformancе tеsting, load tеsting, tеst managеmеnt, and еvеn codе-lеvеl tеst automation.

Candidates must have two years of experience in software testing or a certification covering areas 1 and 2 of the Software Test Professionals Body of Knowledge(STABOK) described by the International Institute for Software Testing.

Exam Overview:

• Rеgistration: Register on the official Intеrnational Institutе for Softwarе Tеsting website.
• Exam Duration: Each module exam typically lasts 60–120 minutes; four modules must be completed.
• Passing Marks: 70% per module
• Cost: $325 per module (total ~$1,300) + $50 graduation fee
• Validity: Lifetime

9. AWS Cеrtifiеd DеvOps Enginееr – Profеssional

Thе AWS Cеrtifiеd DеvOps Enginееr Profеssional cеrtification validatеs an individual’s еxpеrtisе in provisioning, working, and managing distributеd application systеms on thе Amazon Web Services (AWS) platform. It validates thеir ability to bridgе thе gap bеtwееn dеvеlopmеnt and opеrations tеams, еnsuring еfficiеnt and sеcurе softwarе dеlivеry.



Exam Overview:

• Rеgistration: Register on the official AWS Certified DevOps Engineer – Professional website.
• Exam Duration: 180 minutes
• Passing Marks: 750 (on a scale of 100–1000)
• Cost: $300 (may differ based on foreign exchange rates)
• Languages Offered: English, Japanese, Korean, and Simplified Chinese
• Validity: 3 years

10. Manual Software Testing: Complete Course with Practical Labs

Manual Software Testing is a QA software quality assurance certification by Udemy provides a practical lеarning еxpеriеncе, covеring thе basics of softwarе tеsting, tеst planning, tеst casе dеsign, еxеcution, and dеfеct managеmеnt. This helps candidates acquirе a comprеhеnsivе understanding of various tеsting mеthods, such as functional, rеgrеssion, and еxploratory tеsting, through rеal-world еxamplеs and hands-on labs.



Manual Software Testing also addresses еssеntial aspects of tеst documеntation and rеporting, еnabling participants to bеcomе skillеd tеstеrs capablе of еnsuring thе dеlivеry of high-quality softwarе.

Exam Overview:

• Rеgistration: Register on the official Udemy website for this course.
• Course Duration: 13 hours
• Passing Marks: 75%
• Cost: $16.99 USD (Varies depending on discount and exchange rates)
• Validity: Lifetime

In manual testing, QA professionals must understand their role and the broader impact of human judgment on quality assurance. Watch the video below to gain valuable insights.

11. UI Automation and Selectors

UI Automation and Sеlеctors certification is offеrеd by UiPath Acadеmy on Coursеra. This QA cеrtification provides an introduction to UI automation techniques and methods for intеracting with different applications, thе coursе covеrs rеcording usеr actions, gеnеrating workflows, and using sеlеctors for accuratе automation.



Exam Overview:

• Rеgistration: Register on the official Coursera page.
• Course Duration: 8 hours
• Passing Marks: 75%
• Cost: Free
• Validity: Lifetime

12. Automation Test Engineer

Automation Tеst Enginееr Master Program from Simplilearn offers a comprеhеnsivе program in tеst automation, providing in-depth knowledge of Git, Selenium, Jenkins, and JMeter. This QA cеrtification also covеrs kеy framеworks such as Mavеn, TеstNG, Sеlеnium Grid and WеbDrivеr, Dockеr, and Appium.



The program covers over 15 automation testing tools and technologies, including:

• Phase 1: Agile & Scrum methodologies, test case creation
• Phase 2: Functional testing with TestNG, JUnit 5, Gherkin
• Phase 3: Non-functional testing using JMeter and Postman
• Phase 4: Cloud automation with Jenkins, AWS, Docker
• Capstone Project: End-to-end application testing and deployment

Electives include IBM’s courses on Docker Essentials and Introduction to Containers, Kubernetes, and OpenShift V2 .

Exam Overview:

• Rеgistration: Register on the official Simplilearn page for this course.
• Course Duration: 8 months
• Passing Marks: Successful completion of all projects and assessments
• Cost: $651 (may differ based on foreign exchange rates)
• Validity: Lifetime

13. Cеrtifiеd Jеnkins Enginееr (CJE)

Certified Jenkins Engineer is a QA certification еquips you to grasp thе corе idеas bеhind continuous intеgration, continuous dеlivеry, and continuous dеploymеnt. You’ll also havе a clеar understanding of how Jеnkins intеracts with Sourcе Codе Managеmеnt (SCM) systеms.



Furthеrmorе, you’ll gain insights into thе advantages of contributing to thе opеn-sourcе Jеnkins project and lеarn how to participate in its dеvеlopmеnt activеly. This skill sеt not only prеparеs you to navigatе thе landscapе of continuous intеgration but also sеts thе stagе for furthеr еxpеrtisе in Jеnkins and rеlatеd fiеlds.

Exam Overview:

• Rеgistration: Register on the official CloudBees page for this course.
• Exam Duration: 90 minutes
• Passing Marks: 66% (40 correct out of 60)
• Cost: Each exam attempt is $99 (USD)
• Validity: 3 years

14. Responsive Web Design

Rеsponsivе Wеb Dеsign is offered by freeCodeCamp. It helps you build websites and wеbpagеs that adapt to different screen sizes using web technologies like HTML and CSS.



Exam Overview:

• Rеgistration: Register on the official freeCodeCamp page for this QA certification.
• Exam Duration: 300 hours
• Cost: Free
• Validity: Lifetime

15. Performance Testing Using JMeter

Pеrformancе Tеsting Using JMеtеr offered by Edureka. is a QA certification that offers a course that provides valuable insights into understanding softwarе behaviour under various workloads.



Throughout thе coursе, you will gain thе skills to assеss rеsponsе timе and latеncy in softwarе, dеtеrmining its еfficiеncy for scaling. Thе training will еnablе you to еvaluatе thе robustnеss and analyzе thе ovеrall pеrformancе of an application across diffеrеnt load scеnarios.

Exam Overview:

• Rеgistration: Register on the official Edureka page for this course.
• Course Duration: 2 -4 weeks
• Cost: $92

16. Rest API Testing (Automation) from Scratch

If you are aiming to bеcomе proficiеnt in Rеst API automation tеsting, Udеmy Rеst API automation tеsting course is thе idеal choicе for you. Upon completing this QA certification course, you’ll be able to dеvеlop an API automation framework using Rеst Assurеd API and acquirе in-depth knowledge of tools such as Postman.



Exam Overview:

• Rеgistration: Register on the official Udemy page for this course.
• Exam Duration: 28 hours
• Cost: $38

These are some of the best QA certifications you can get certified into and take your career to the next level. Before you begin to take certification, it’s important to refresh your existing automation skills. The LambdaTest YouTube Channel provides manual and automation testing tutorials that can help polish your test automation skills.

Why Do Organizations Onboard Certifed QA Testers?

Organizations hire certified QA testers to ensure proven skills, reduce training costs, boost team knowledge, provide accurate testing solutions, and maximize ROI with up-to-date expertise.

Thеsе days, organizations prefer hiring cеrtifiеd softwarе tеstеrs, and hеrе’s why:

• Candidatеs with tеsting cеrtifications bring a cost-saving advantage by minimizing training and onboarding еxpеnsеs. Employеrs fееl morе sеcurе hiring cеrtifiеd softwarе tеstеrs bеcausе thеy comе with provеn skills, capablе of analyzing, implеmеnting, and mеasuring еffеctivе tеsting solutions.
• Cеrtifiеd QA tеstеrs play the role of valuablе mеntors, sharing thеir еxpеrtisе and motivating thеir collеaguеs. Thеir cеrtifications signify up-to-date skills, making thеm instrumеntal in boosting thе collеctivе knowlеdgе of thе tеam rеgarding tеst covеragе and rеquirеmеnts.
• Candidatеs with QA cеrtifications arе considеrеd assеts to an organization. Their continuous skill dеvеlopmеnt, along with a mix of businеss and tеchnical know-how, contributes to maximizing thе organization’s rеturn on invеstmеnt.
• Cеrtifiеd softwarе tеstеrs often undеrgo thorough assеssmеnts tests, еnsuring thеir rеal-timе knowlеdgе and compеtеncе. Whеn an organization hirеs a cеrtifiеd QA tеstеr, thеy can have a sense of confidence that thе solutions providеd will be accurate and bеnеficial, aligning with thе organization objеctivеs.

Who Should Take Quality Assurance Certifications?

QA certifications are ideal for freshers, career changers, professionals aiming for growth, or anyone seeking industry recognition and validation of their testing knowledge and skills.

Whеthеr you should obtain QA cеrtifications dеpеnds on sеvеral factors, including your carееr goals, currеnt еxpеriеncе, and dеsirеd focus within thе fiеld. Hеrе’s a brеakdown to hеlp you dеcidе:

• Frеshеrs with limitеd еxpеriеncе: Entry-lеvеl cеrtifications provide a solid foundation in tеsting principlеs and procеssеs.
• Carееr changеrs or thosе transitioning into QA: Cеrtifications dеmonstratе your commitmеnt to thе fiеld and can bridgе thе gap in еxpеriеncе.
• Profеssionals sееking job advancеmеnt: Highеr-lеvеl cеrtifications in spеcific arеas likе web automation or mobile automation tеsting can showcasе your еxpеrtisе and еnhancе your candidacy for lеadеrship rolеs.
• Individuals looking for industry recognition: Few cеrtifications hold weight in specific industries, potentially impacting your job prospеcts and incomе.
• Anyonе sееking to validatе thеir knowlеdgе and skills: Cеrtifications offеr еxtеrnal validation of your еxpеrtisе, giving you confidеncе and crеdibility in your rolе.

What are Some Skills QA Testers Should Have?

QA testers should master programming languages, automation frameworks, web locators, Agile and DevOps practices, and have experience in both manual and automated testing on local and cloud environments.

Below are some must-have skills for an automation tester before attempting any test automation certification:

• Proficiency in programming languages like Java, JavaScript, Python, C#, Ruby, etc.
• Know-how of automation testing frameworks like Selenium, Playwright, Cypress, Appium, and more.
• In-depth understanding of the DOM and web locators in different automation testing frameworks.
• Know-how of Agile and DevOps methodologies.
• In-depth understanding of web or mobile app testing using different languages and frameworks.
• Experience running manual and automated tests (serial and parallel) on local or cloud grids.

Now, let’s discover some of the Top Quality Assurance Certification you can take in 2025.

Time to Upskill

If you arе a bеginnеr, you can dеfinitеly еxplorе somе of thе best QA cеrtifications listеd abovе and lеarn from basics to advancеd modulеs, wе hope you will find this bеst QA cеrtifications helpful. Go for thе onе that suits your nееds and procееd on to bеcome an еxpеrt in thе domain of tеst automation.

Frequently Asked Questions (FAQs)

What is thе bеst cеrtification for QA?

Thе bеst cеrtification for QA largеly dеpеnds on your spеcific carееr goals and thе industry you arе in. Somе widеly rеcognizеd cеrtifications includе ISTQB (Intеrnational Softwarе Tеsting Qualifications Board), CSTE (Cеrtifiеd Softwarе Tеstеr), CSQA (Cеrtifiеd Softwarе Quality Analyst), and LambdaTest Certifications. Rеsеarch еach onе to dеtеrminе which aligns bеst with your nееds.

Which course is best for QA?

Thе bеst coursе for QA dеpеnds on your background, еxpеriеncе, and lеarning prеfеrеncеs. Onlinе platforms likе Udеmy, Coursеra, and LinkеdIn Lеarning offеr various coursеs on softwarе tеsting and QA. Look for courses that covеr tеsting mеthodologiеs, tools, and industry bеst practices.

What is QA cеrtifiеd?

QA cеrtification gеnеrally rеfеrs to bеing cеrtifiеd in softwarе quality assurancе. This can include cеrtifications which validatе your knowledge and skills in еnsuring thе quality of softwarе through еffеctivе tеsting procеssеs.

Which certificate is best for QA?

Some of the best QA certifications for professionals include:

• LambdaTest Certifications (Selenium, Playwright, Cypress, Appium)
• ISTQB (International Software Testing Qualifications Board)
• IIST (International Institute of Software Testing)
• CSTE (Certified Software Testing Engineer)
• CSQA (Certified Software Quality Analyst)
• Simplilearn Automated Test Engineer Certification
• Edureka Performance Testing Using JMeter

Among these, LambdaTest Certifications stand out for offering free, AI-driven, real-device testing certifications that help professionals validate their automation and manual testing skills effectively.

How to get a QA QC certificate?

To get a QA QC (Quality Assurance and Quality Control) certificate, follow these steps:

1. Choose a recognized certification: such as LambdaTest QA Certifications, ISTQB, CSTE, or CSQA.
2. Meet the eligibility requirements: usually basic knowledge of software testing or relevant experience.
3. Complete the required training or course: many programs offer online, self-paced learning.
4. Register and pass the certification exam: most tests include both theoretical and practical assessments.
5. Get certified and maintain validity: some certifications require renewal after a few years.
]]> https://www.lambdatest.com/blog/september-2025-updates/ Mon, 06 Oct 2025 14:04:19 +0000 https://www.lambdatest.com/blog/?p=92554
TABLE OF CONTENTS
• LambdaTest Credits Management System
• New KaneAI Features
• Latest HyperExecute Features
• Enhanced Features in Insights
• SmartUI Auto Port Switch
• AI-Native Smart Heal for App Automation
• iOS App Accessibility Scanner

We’re back with another round of product updates to enhance your testing experience.

LambdaTest now lets you allocate and track credits for AI-native and premium features. With KaneAI, you can generate complete test plans and step-by-step test cases automatically from high-level objectives. HyperExecute provides visibility into retries, network traffic, muted tests, and local service access. Insights allows you to schedule reports directly to Email, Slack, or Microsoft Teams.

SmartUI CLI automatically resolves port conflicts during visual testing. AI-native smart heal detects broken locators in mobile tests and applies valid alternatives in real time. The iOS Manual App Scanner identifies accessibility issues and provides actionable fixes.

Live With Credit Management Systems

We have introduced a new Credits system for accessing AI-native features and premium capabilities across the LambdaTest platform. This system provides flexible usage options, ensuring that you can take full advantage of advanced features within your subscription limits. With credits, you gain a clear mechanism to manage resources efficiently while maintaining control over your testing workflows.

Admins have the ability to manage credit allocation across your organization and monitor usage through the Billing and Subscriptions Page. Organizations can also set soft limits for credit usage. You can continue using features even after reaching the limit, while receiving notifications to maintain visibility.

• Centralized management: Admins can allocate credits and track consumption across the organization through the Billing and Subscriptions Page.
• Credit limits and alerts: Soft limits can be configured, and notifications are sent to maintain visibility even when thresholds are reached.
• Flexible usage: Credits can be applied to AI-native features and premium capabilities according to your testing needs.
• Business continuity: The system ensures uninterrupted testing while supporting organizational budget management.

For more detailed instructions on managing credits, you can refer to the guide on credit management.

New KaneAI Features and Enhancements

With LambdaTest KaneAI, you can generate test cases effortlessly and validate them before execution, reducing manual effort and preventing false positives. The platform helps you manage test sessions in real time and optimize team workflows, ensuring tests run efficiently across applications and environments.

Revamped Test Authoring in KaneAI

KaneAI now makes test creation faster and more reliable with its revamped test authoring capabilities. You can generate a comprehensive test plan from your inputs before executing tests, ensuring accuracy and reducing errors. This allows you to review workflows in advance, streamline test preparation, and maintain consistent coverage across your automation suite.

Plan Tests Efficiently With Enhanced Test Authoring

With enhanced test authoring, you can have KaneAI analyze your inputs upfront and generate a comprehensive test plan before producing executable test cases. This allows you to review the AI-generated workflow early and make adjustments where necessary.

By validating the plan before execution, you can reduce the risk of misaligned coverage or false positives, ensuring your automated tests reflect the behavior you intend to test. You can also catch potential errors before running your tests, saving time and avoiding unnecessary debugging during execution.

This feature is particularly valuable if your tests are complex or cover multiple workflows. You can ensure that every step is logically aligned and consistent with your testing objectives without manually planning every detail.

Accelerate Test Creation With Generative Authoring

You can provide high-level objectives to KaneAI, and it will automatically generate the full set of steps required for your test cases. By leveraging historical execution patterns and best practices, KaneAI fills in gaps in your workflow, maintaining accuracy and consistency across your test suite.

This allows you to focus on higher-value activities such as reviewing results, refining test logic, or exploring edge cases instead of spending time manually creating each test step. You can accelerate test creation without compromising reliability, and your team can scale testing more effectively while ensuring consistent coverage across multiple applications or environments.

Maximize Resource Visibility With KaneAI Sessions Dashboard

With the KaneAI Sessions dashboard, you can track active, queued, and pending test sessions in real time. You can monitor session status, allocate resources effectively, and manage monthly quotas to maximize throughput. This visibility allows you to avoid conflicts when multiple team members are running tests concurrently and ensures that your team can prioritize critical sessions efficiently.

By using the dashboard, you can make informed decisions about scheduling, resource utilization, and workflow optimization. You can also identify bottlenecks early, balance workloads across your team, and maintain seamless test execution even in large-scale testing environments.

Optimize Team Workflow With Licensing and Concurrency in KaneAI

You can manage KaneAI licenses at the organization level, with concurrent agent support to run multiple sessions simultaneously. Each license includes a premium Test Manager entitlement, which your admin can allocate according to team priorities.

This allows you to streamline collaboration, ensure team members have access to the resources they need, and avoid delays caused by license constraints. You can plan testing schedules confidently, allocate tasks efficiently, and maintain full visibility into resource usage, ensuring that your testing operations scale smoothly as your team grows.

Additional KaneAI Enhancements to Boost Productivity

Here are some additional enhancements in KaneAI that can help you maximize automation throughput while ensuring accuracy and consistency across your test suite.

• AI Test Case Generator integration: Generate and automate multiple test cases directly within KaneAI, reducing manual setup, accelerating test execution cycles, and improving overall throughput.
• Network log assertions for web tests: Assert API request and response payloads during web tests, enabling more thorough verification of backend interactions and improving end-to-end test reliability.
  

Check out this guide on network log assertions in KaneAI.

Explore Generative AI Testing Effortlessly With KaneAI Freemium

You can now experience the power of Generative AI testing without any setup barriers through the KaneAI freemium plan. This free tier gives you hands-on access to KaneAI’s core capabilities, allowing you to explore intelligent test generation, automated session management, and workflow orchestration before committing to a full license.

With two AI Agent sessions, two Test Manager Seats, and 30 days of unrestricted access, you can evaluate how KaneAI fits into your testing pipeline. It’s a practical way to understand the benefits of AI automation like faster test creation, improved coverage, and reduced manual overhead—while maintaining full visibility into results and collaboration workflows.

Latest Features in HyperExecute

HyperExecute is designed to help you execute tests at scale with full visibility and control, whether you are running on real devices, emulators, or complex web environments. With a suite of enhancements, you can capture network traffic, monitor retries, manage muted tests, bypass proxy issues, and track execution live, all from a single dashboard.

Capture Network Traffic Easily With MITM Support in Emulators

With Man-in-the-Middle (MITM) proxy support, you can capture network logs directly from emulator sessions. By enabling the mitmProxy: true flag in your hyperexecute.yaml file, you can analyze API calls, requests, and responses during test execution.

framework:
  name: raw
  args:
    mitmProxy: true

This feature allows you to debug complex interactions between your application and backend services without manual interception. You can identify hidden issues, verify request payloads, and ensure that your tests reflect real-world scenarios accurately.

MITM support is available for all emulators, giving you the flexibility to test across multiple devices and configurations while maintaining full visibility into network behavior.

Track Retries Clearly With Enhanced HyperExecute Reports

You can now gain better visibility into test retries at both the summary and test-case level. The updated reports refine total counts by excluding retries and display retry indicators alongside each scenario or test. In the “Test Cases” view, retried tests are marked clearly with a retry icon, making it easy for you to differentiate between original executions and repeated attempts.



This enhancement allows you to analyze failures more accurately, identify flaky tests, and optimize your automation suite without misinterpreting the results. You can focus on unique test outcomes while still keeping track of retries, helping your team improve test reliability and reduce wasted cycles.

Manage Muted Tests Efficiently With Bulk Unmute

With the muted test count and bulk unmute feature, you can now view the total number of muted tests in your suite and unmute all tests at once. Previously, reactivating muted tests required manual intervention, which was time-consuming and prone to oversight.

This update allows you to quickly restore tests for execution, streamline test management, and maintain control over your suite. You can reactivate tests after fixing issues or updating workflows, ensuring that no critical scenario is left untested due to muted status.



Bypass Proxy For Local Services With bypassProxyDomains

You can now use the HyperExecute bypassProxyDomains capability to exclude specific domains from Dedicated Proxy usage. This ensures that local services such as localhost, 127.0.0.1, or internal endpoints remain accessible during test execution when dedicatedProxy: true is enabled.

This capability allows you to test local APIs or staging environments without modifying proxy configurations. You can maintain secure access to internal resources while leveraging a Dedicated Proxy for external endpoints, ensuring that network routing is accurate and tests remain stable.

Monitor Test Execution Live With Live Command Logs

With live command logs, you can see test execution logs in real-time directly in the HyperExecute dashboard. Previously, logs appeared in chunks, which delayed visibility and made debugging harder. Now you can monitor every command as it executes, allowing you to identify issues immediately and react quickly.

This feature enhances your ability to troubleshoot, optimize tests, and maintain transparency across your automation pipeline. You can track progress, validate steps on the fly, and ensure that your team remains aware of test outcomes without waiting for execution to complete.

Enhanced Features in Insights

We have rolled out new features in LambdaTest Insights that give you full visibility into build performance, test results, and team efficiency, enabling you to identify bottlenecks, prioritize critical issues, and continuously improve your testing strategy.

Get Reports Instantly With Custom Notification Scheduling

With custom notification Scheduling, you can set up weekly or monthly reports to arrive at the day and time that suits your workflow. Whether you prefer updates every Wednesday or on the 15th of each month, you can stay informed without manually checking dashboards.

You can choose your preferred communication channels, such as Email, Slack, or Microsoft Teams, so your team receives critical updates in the tools they already use. This allows you to monitor test outcomes, track progress, and respond to issues promptly.

To enable, visit the Product Preferences section of your Account Settings and then nvaigate to Analytics and enable the Enable build completion emails toggle.



Benefits:

• Stay informed automatically: Receive updates without opening dashboards.
• Align your team: Share key metrics with stakeholders instantly.
• Reduce manual effort: Eliminate the need to track reports manually.
• Plan effectively: Schedule notifications to match your workflow or team routines.

Monitor Build Performance With Insights Email Notifications

With build insights email notifications settings, you can receive automated summaries of build performance, test results, and failure classifications directly in your inbox. This allows you to act immediately on failures and monitor progress without constantly checking the dashboard.

You can track success/failure rates, test coverage, performance metrics, and recurring issues, giving you a clear understanding of your builds. The notifications include direct links to detailed build analysis, so you can investigate problems quickly, share insights, and prioritize critical issues efficiently.

Benefits:

• Detect failures faster: Identify issues the moment they occur.
• Maintain stakeholder visibility: Share actionable insights without extra effort.
• Improve test efficiency: Focus on critical failures instead of sifting through all results.
• Enhance operational control: Monitor trends and performance across builds for better planning.

Streamline Visual Testing With SmartUI Auto Port Switch

With SmartUI CLI auto port switch, you can automatically handle port conflicts when starting the SmartUI snapshot server. This feature ensures that your visual testing scripts run without interruption, even if the default port is already in use. By automatically selecting an available port, you can avoid errors, failed builds, or delays caused by manual port management.

This capability is particularly useful when you run multiple tests in parallel or execute visual tests on shared environments. You can maintain consistent test execution without having to manually check or configure ports each time you run your scripts.

To get started, check out this SmartUI CLI Exec guide.

Benefits:

• Automatic conflict resolution: You can run your CLI commands without worrying about port conflicts.
• Seamless parallel execution: Multiple tests or sessions can run concurrently without interference.
• Reduced manual intervention: No need to manually update port numbers in configuration files.
• Reliable visual testing: Ensures the snapshot server is always accessible, improving overall test stability.

Keep Mobile Tests Stable With AI-Native Smart Heal

With Smart Heal in Appium automation, you can handle locator failures automatically during mobile test automation, reducing manual effort and ensuring that your tests continue running smoothly even when the UI changes. This AI-native feature intelligently detects missing elements, analyzes the UI in real time, and applies the closest valid match, helping you maintain test reliability without constant intervention.



You can also view full visibility into healing actions through detailed logs and the LambdaTest App Automation dashboard. This transparency allows you to understand what changes Smart Heal applied, why a locator was healed, and how your test flow was adjusted. By keeping tests stable, you can focus on higher-value validation tasks instead of repeatedly fixing broken locators.

Benefits:

• Automatic locator detection: You can detect missing or broken locators in real time, preventing unnecessary test failures.
• Intelligent UI analysis: Smart Heal evaluates the interface and selects the best alternative element, keeping your tests on track.
• Fallback suggestions: When automatic healing isn’t possible, you can get actionable recommendations to fix locators quickly.
• Runtime recovery: You can automatically update failing steps to ensure continuous test execution.
• Full transparency: Logs and dashboards show both original and healed locators, giving you complete insight into test adjustments.
Note

Run mobile automated tests across 10000+ real devices. Try LambdaTest Today!

Ensure Inclusive Apps With iOS Manual App Scanner

With LambdaTest manual iOS App Scanner, you can now extend accessibility testing to iOS applications just like you do on Android. This feature allows you to identify accessibility issues in real time as you interact with your app, ensuring that your mobile applications meet standards such as WCAG and are usable for everyone, including people with disabilities.



By integrating this scanner into your manual testing workflow, you can detect issues as they occur, prioritize fixes based on severity, and provide detailed remediation guidance to your development team. This ensures that accessibility is not an afterthought but an integral part of your mobile testing process.

To begin with, refer to this documentation on accessibility app scanner.

Benefits:

• Real-time scanning: You can catch accessibility issues while navigating the app, preventing overlooked problems.
• Comprehensive categorization: Issues are organized by severity, helping you focus on the most critical fixes first.
• Detailed remediation guidance: Each detected problem comes with suggested solutions, so you can resolve issues efficiently.
• Multi-format reporting: You can export findings in JSON, CSV, or PDF formats for documentation and sharing.
• Seamless workflow integration: You can test directly on real iOS devices within LambdaTest, without switching platforms.

Wrapping Up!

With the above latest features and enhancements, you can accelerate test creation, maintain stability, and gain full visibility across platforms. KaneAI helps you generate accurate tests effortlessly, HyperExecute keeps your executions transparent, Smart Heal ensures mobile tests run smoothly, SmartUI simplifies visual testing, and iOS Manual App Scanner improves accessibility coverage. These features work together to reduce manual effort, improve reliability, and scale your testing efficiently, enabling you to deliver high-quality applications faster and with confidence.

]]> https://www.lambdatest.com/blog/product-launch-checklist/ Wed, 01 Oct 2025 10:21:03 +0000 https://www.lambdatest.com/blog/?p=92627

Launching a product can seem like a big task with many things to keep track of. A simple product launch checklist makes it easier by breaking down the key steps you need to take. With this checklist, you can stay organized, ensure nothing is missed, and set your product up for a successful launch.

Overview

A product launch checklist is a structured guide that helps streamline the process of bringing a new product to market. It ensures that all critical steps are covered, from pre-launch preparations to post-launch monitoring, ensuring a smooth and successful introduction.

Types of AI in Product Launch

Key details about AI in product launches:

• AI-powered Test Automation: Automates repetitive testing tasks and speeds up test cycles.
• Predictive AI Insights: Analyzes data to predict potential issues before they arise.
• AI-driven Orchestration: Orchestrates test environments and automates workflows for more efficient testing.
• Continuous Monitoring: Uses AI to monitor performance, track errors, and predict fixes in real time.

AI in Product Launch Checklist

How AI enhances the product launch checklist:

• Pre-Launch: Integrate AI tools to automate test case generation, predict test outcomes, and ensure comprehensive test coverage.
• Launch Day: Use AI-driven tools to monitor the product’s real-time performance, detect anomalies, and provide predictive insights into potential issues.
• Post-Launch: Leverage AI for continuous performance optimization, analyzing user feedback, and identifying areas for improvement.

By incorporating AI into your product launch checklist, teams can reduce testing time, minimize errors, and ensure a smoother, faster, and more successful product launch.

TABLE OF CONTENTS

• What Is a Product Launch?
• Why a Product Launch Checklist is Essential
• Pre-Launch: The Foundations of a Successful Product Launch
• Launch Day: Executing the Plan
• Post-Launch: Monitoring and Continuous Improvement
• Common Challenges and How to Overcome Them
• Best Practices for Product Launch
• Conclusion
• Frequently Asked Questions (FAQ)

What Is a Product Launch?

A product launch is the process of introducing a new product to the market to generate interest, drive sales, and establish its place in the market. It involves planning, preparation, and execution. Using a product launch checklist helps you stay organized and ensures no key steps are missed, from defining goals to promoting your product and handling post-launch feedback. This checklist is essential for a smooth, successful launch.

Why a Product Launch Checklist is Essential

Before diving into the specifics, it’s crucial to understand why having a checklist is so important. A product launch checklist streamlines communication, ensures all team members are aligned, and minimizes risks. It serves as a structured guide, enabling teams to efficiently manage tasks, track progress, and spot potential issues early in the process.

Pre-Launch: The Foundations of a Successful Product Launch

The pre-launch phase is critical, setting the stage for everything that follows. It includes preparation for testing, marketing, and internal alignment. This section covers:

Finalizing Product Requirements

• Ensure all product features are clearly defined and aligned with business goals. Review product specifications, user stories, and design documents. Any last-minute changes should be addressed here.

Setting Up Testing Infrastructure

• As a QA engineer, ensure that your testing infrastructure is in place. This means setting up environments, configuring test management tools, and preparing automation scripts.
  Cloud testing platforms like LambdaTest are essential for web testing, offering features such as cross-browser testing and mobile app testing to ensure your website or app performs consistently across various environments. It also supports debugging by providing video recordings, console logs, and network logs, allowing developers to quickly identify and resolve issues without the need to reproduce them.

Testing Coverage Planning

• Define the scope of testing, including functional, regression, performance, and security testing. Use LambdaTest’s AI-native test orchestration capabilities to ensure that you cover every possible scenario.

Integration with CI/CD Pipelines

• Continuous integration and delivery (CI/CD) tools are key for automating testing and deployment. Ensure that your testing processes are integrated into your CI/CD pipeline to catch defects early in the process.

Launch Day: Executing the Plan

On launch day, the focus shifts to real-time testing and monitoring. Having a clear plan for launch day ensures that everything runs smoothly.

Real-Time Monitoring During Launch

• Set up monitoring tools to track the performance of your application in real time. Pay attention to key metrics such as load times, user interactions, and error rates. Tools like LambdaTest’s Test Insights can offer predictive insights into performance issues before they arise.

Handling Critical Bugs and Issues

• Have a process in place to address high-priority bugs immediately. This includes clear communication channels and predefined workflows for fast bug fixes.

Collaboration with Cross-Functional Teams

• QA engineers must work closely with developers, product managers, and operations teams during the launch. Ensure that everyone is on the same page regarding the current status of the product and any issues that may arise.

Post-Launch: Monitoring and Continuous Improvement

The launch doesn’t end with the product going live. Post-launch activities are essential for maintaining quality and ensuring customer satisfaction.

Gathering User Feedback

• Collect real-time user feedback through support channels, surveys, and social media to identify any unexpected issues. This will help you prioritize patches and updates.

Post-Launch Testing and Bug Fixing

• Continue testing after the launch to identify any bugs that were missed during pre-launch testing. Use LambdaTest’s Test Insights to get a deep analysis of flaky tests, error trends, and areas for improvement.

Performance Optimization

• Monitor performance metrics post-launch to ensure that the product is operating efficiently. Optimize any areas that show performance degradation.

Common Challenges and How to Overcome Them

Every product launch comes with its set of challenges. Addressing them early can ensure a smoother transition from development to launch.

Handling Time Constraints

• With tight deadlines, testing may get rushed. Focus on optimizing your test cycles using AI-driven test orchestration like LambdaTest’s HyperExecute, which reduces testing time by up to 70%.

Limited Resources and Budget

• Work with your team to prioritize critical tests that align with business goals. Cloud-based testing platforms like LambdaTest allow you to scale resources as needed without the overhead of maintaining infrastructure.

Managing Complex Product Features

• As products become more complex, ensure that your test coverage includes all possible scenarios. AI-driven testing platforms like LambdaTest can help automate repetitive tasks, freeing up your team to focus on more complex testing.

Best Practices for Product Launch

Launching a product successfully requires more than just a great idea. It’s about preparation, strategy, and execution. To guide you through this process, a well-organized product launch checklist is key. Here are some best practices to ensure your product launch goes smoothly:

1. Know Your Audience: Before you launch, make sure you clearly understand your target audience. Research their needs, pain points, and preferences. Tailoring your launch strategy to meet their expectations will increase engagement and excitement. A product launch checklist can help ensure you don’t miss out on crucial audience research.
2. Create a Solid Marketing Plan: Plan your marketing efforts well in advance. Build anticipation by teasing the product on social media, through email campaigns, and on your website. Consider offering exclusive early access or special promotions to build hype. Your product launch checklist should include marketing tactics and timing to help you stay on track.
3. Ensure Product Readiness: Ensure that your product is fully tested and ready for the public. Conduct quality checks, user testing, and gather feedback from beta testers to iron out any issues before the official launch. Nothing damages credibility faster than a faulty product. Make sure your product launch checklist includes thorough product testing before going live.
4. Set Clear Goals: Define clear, measurable goals for your launch. Whether it’s sales numbers, website traffic, or social media engagement, having specific objectives will help guide your strategy and track progress. A product launch checklist will help you break down these goals into actionable steps.
5. Leverage Influencers and Partnerships: Collaborate with influencers, partners, or affiliates who align with your brand. Their reach can help amplify your launch, generating buzz and attracting a wider audience. Adding this to your product launch checklist ensures you don’t overlook valuable partnerships.
6. Prepare for Feedback and Adjustments: Be ready to handle feedback from your customers. It’s important to listen, whether the feedback is positive or critical. Address issues promptly and update your product or marketing strategies accordingly to keep momentum going. Your product launch checklist should include strategies for collecting and acting on customer feedback.
7. Post-Launch Follow-up: After your launch, keep the excitement going with follow-up campaigns. Continue to engage with your audience through email newsletters, social media posts, and additional promotions. Don’t let the buzz fade after the initial launch phase. Make sure your product launch checklist includes post-launch activities to maintain customer engagement.

By following these best practices and using a well-structured product launch checklist, you can set your product up for a successful launch and ensure it continues to thrive post-launch.

Conclusion: Launching a Successful Product Starts with QA

A well-executed product launch checklist is crucial for the success of any product. By focusing on the pre-launch preparation, on-the-day execution, and post-launch monitoring, teams can avoid costly mistakes and ensure that the product meets customer expectations. QA Engineers play a pivotal role in this process, ensuring that every aspect of the product has been thoroughly tested, optimized, and validated before it reaches the market.

Frequently Asked Questions (FAQs)

What is a Product Launch Checklist?

A product launch checklist is a detailed guide that outlines all the essential tasks and activities needed to successfully launch a product. It helps ensure that nothing is overlooked and that the launch process runs smoothly.

Why is a Product Launch Checklist important?

A checklist helps organize tasks, ensure all steps are followed, reduce the risk of errors, and streamline communication among team members. It ensures every detail is considered and the product reaches the market successfully.

What should be included in a Product Launch Checklist?

A comprehensive product launch checklist includes pre-launch tasks (product requirements, testing, marketing strategy), launch day activities (real-time monitoring, bug fixes), and post-launch follow-up (feedback gathering, performance tracking).

How do QA Engineers contribute to the Product Launch Checklist?

QA Engineers ensure that the product is thoroughly tested before launch. They are responsible for verifying product functionality, performance, and security, identifying bugs, and ensuring that the product meets customer expectations.

How do you prepare for the testing phase in a Product Launch Checklist?

Prepare by setting up test environments, defining test plans, automating test cases, and ensuring that the testing coverage includes functional, regression, and performance testing. This step also includes integrating testing into the CI/CD pipeline for continuous validation.

What role does automation play in the Product Launch Checklist?

Automation speeds up the testing process, ensuring more extensive coverage in less time. It helps with regression testing, performance checks, and handling repetitive tasks, making the testing phase more efficient and reducing human error.

How do I handle product issues during the launch?

Set up real-time monitoring and have a team in place to address issues as they arise. This could include quickly identifying and fixing critical bugs, resolving performance issues, and ensuring communication between the development, QA, and product teams to implement quick fixes.

What tools can help with a Product Launch Checklist?

Tools like Jira for task management, LambdaTest for test execution, Selenium for automation, and TestRail for test case management help streamline the product launch process by offering structured workflows and integrating different stages of the launch.

How can I optimize my post-launch activities?

Post-launch activities should focus on monitoring the product’s performance, gathering user feedback, and addressing any issues that arise. Use monitoring tools to track key metrics and leverage automated testing to identify potential problems quickly.

How does AI assist in a Product Launch Checklist?

AI tools, like LambdaTest’s HyperExecute and KaneAI, can help accelerate testing cycles, detect anomalies, automate the creation of test cases, and provide predictive insights. These AI-driven tools improve product quality, minimize human error, and speed up the entire launch process.

]]> https://www.lambdatest.com/blog/visual-accessibility-web-scanner/ Wed, 01 Oct 2025 07:34:34 +0000 https://www.lambdatest.com/blog/?p=92540

Shipping fast breaks UI in small ways that users feel first: shifted buttons, clipped text, missed alt tags, contrast failures. At scale, catching these across pages, viewports, and releases is tedious and easy to miss especially behind logins or on staging.

LambdaTest Web Scanner simplifies testing with one-click execution of scheduled Visual UI and WCAG scans, covering public and private sites, and routing results to SmartUI and Accessibility dashboards for quick triage and audit-ready reports.

Let’s dive deep into what makes Web Scanner a game-changer for your testing workflows!

What is LambdaTest Web Scanner?

LambdaTest Web Scanner is your all-in-one solution for proactive quality assurance. It enables organizations to catch visual bugs and accessibility violations across thousands of URLs at scale, without the manual overhead that typically comes with comprehensive web testing.

Whether you’re a QA team running nightly regression scans, an accessibility engineer validating WCAG compliance, or a product owner monitoring web releases, Web Scanner empowers you to maintain the highest standards of quality and inclusivity.

Key Features:

• Automated Visual UI Regression Testing powered by SmartUI
• WCAG 2.0/2.1 AA Accessibility Audits
• Smart scheduling for one-time or recurring scans
• Cross-browser testing across Chrome, Firefox, Edge, and Safari
• Support for authenticated pages and local environments
Note

To learn more about web scanner, check out our detailed support documentation.

Why use a Web Scanner?

Manual testing of large-scale web applications for layout shifts, broken UI elements, or WCAG non-compliance is time-consuming, error-prone, and simply doesn’t scale. Here’s how Web Scanner transforms your testing approach:

• Eliminate Manual Bottlenecks: Say goodbye to hours spent clicking through pages and comparing screenshots manually. Web Scanner automates the entire process, freeing your team to focus on strategic testing initiatives.
• Catch Issues Before Users Do: With scheduled scans and instant alerts, you’ll identify visual regressions and accessibility violations before they impact your users’ experience.
• Ensure Compliance at Scale: For organizations that need to maintain WCAG compliance across hundreds or thousands of pages, Web Scanner provides audit-ready reports that make compliance verification effortless.
• Save Time and Resources: Automate what used to take days into a process that runs in hours, with results delivered directly to your SmartUI and Accessibility Dashboards.

Visual UI Scanning

Visual scans detect pixel-based layout changes, design mismatches, missing elements, or unintended visual regressions by comparing screenshots taken at scheduled intervals.

Key Capabilities:

• Cross-Browser Validation: Test UI across Chrome, Firefox, Edge, and Safari.
• Responsive Testing: Check layouts on 8 desktop sizes and 200+ mobile viewports (Android & iOS).
• Custom Configurations: Override defaults with your own SmartUI JSON settings.
• Accurate Screenshots: Use delays to capture pages with animations or dynamic content.
• History & Comparison: Access past scans to track changes, compare builds, and spot regressions.

Accessibility Scanning

Accessibility scans audit your pages against WCAG 2.0/2.1 standards, surfacing violations and recommendations to improve inclusivity.

Key Features:

• WCAG 2.0/2.1 AA Compliance: Comprehensive audits against accessibility standards.
• “Needs Review” Toggle: Flags issues requiring human verification to reduce false positives.
• Best Practices Flagging: Recommendations beyond WCAG compliance.
• Deep Scan Reports: Filter by severity (Critical, Serious, Moderate, Minor).
• Smart Filters: Identify recurring violations across multiple pages.
• Exportable Reports: Generate audit-ready documentation with code snippets and recommended fixes.

All In All!

Discover the power of automated visual and accessibility testing with LambdaTest Web Scanner! Whether you’re running regression checks or validating WCAG compliance across thousands of pages, Web Scanner streamlines your workflow and helps you catch issues before they reach production.

We value your feedback – share your thoughts on Web Scanner with us through the LambdaTest Community or by emailing support@lambdatest.com.

Happy Testing!

]]> https://www.lambdatest.com/blog/voice-ai/ Tue, 30 Sep 2025 19:45:48 +0000 https://www.lambdatest.com/blog/?p=92486
TABLE OF CONTENTS
• $62 Billion Problem That Voice AI Solves
• The Voice AI Market Explosion
• Why 99.7% Reliability Matters?
• ROI That Makes CFOs Happy
• What Success Looks Like In Implementation?
• Window of Opportunity for Competitive Advantage
• Critical Testing Phase: Ensuring Voice AI Reliability
• 2025 Business Reality Check
• Broader Business Impact
• Technology Behind the Magic
• Common Implementation Pitfalls to Avoid
• Future Is Voice: What’s Next
• Frequently Asked Questions (FAQs)

The change from reactive to proactive customer service is here. Voice AI agents are not only changing how businesses work, but they are also completely changing what customers expect.

Overview

Voice AI is technology that understands, processes, and responds to human speech, enabling tasks like virtual assistants, customer service automation, and real-time voice-driven interactions.

What Problems Does Voice AI Solve?

Voice AI solves critical business challenges by answering missed calls, capturing leads, recovering lost revenue, and automating phone interactions efficiently.

• Missed calls: Small businesses often miss 40% of calls during peak hours, losing sales and trust; Voice AI answers every call automatically.
• Leads lost: Home services lose 27% of inbound calls, limiting conversions; Voice AI greets, qualifies, and routes every caller efficiently.
• Revenue gap: Businesses forfeit over $126,000 annually due to unanswered calls; Voice AI recovers missed revenue by handling each interaction reliably.
• Phone reliance: With 80% of communications over the phone, missed calls disrupt operations; Voice AI manages calls consistently, reducing inefficiencies and lost opportunities.
• Automation: Voice AI answers, qualifies, and routes calls instantly, ensuring no leads are lost, boosting sales, improving customer experience, and enhancing efficiency.

Why Is Testing Critical for Voice AI Reliability?

Before a voice AI interacts with real customers, thorough testing is essential. Platforms like LambdaTest Agent to Agent Testing lets developers and testers rigorously test voice AI agents across real world scenarios.

It enables:

• Automated Call Testing: Simulates hundreds of calls, verifying AI responses in realistic scenarios, ensuring accuracy and consistent behavior before real customer interactions.
• Edge Case Validation: Identifies complex or rare customer situations that could cause failures, allowing AI behavior to be refined for reliability.
• Performance Under Load: Tests high call volumes to ensure AI maintains response speed, accuracy, and quality even during peak operational periods.
• Integration Testing: Verifies smooth handoffs between AI and human agents, preventing dropped calls and ensuring seamless customer experiences.
• Multi-scenario Testing: Validates workflows like bookings, complaints, sales, and technical support, ensuring AI performs consistently across all customer interaction types.

The $62 Billion Problem That Voice AI Solves

Imagine this: it’s a busy Tuesday at 2:30 PM. A potential customer calls your business and says they want to buy something for $5,000. The phone rings once, twice, three times… no response. They hang up, call your competitor, and never think about your business again.

This happens millions of times every day in the United States. The numbers are shocking and show a big chance that most people miss. This underscores the growing for voice agents.

The Missed Call Crisis: Real Numbers, Real Effects

Global forecasts show exponential growth, confirming that voice AI is more than a trend, it is a business-critical technology that early adopters can leverage.

• Small businesses miss up to 40% of incoming calls during peak hours when team members are occupied with other tasks. – U.S. Chamber of Commerce.
• Home service businesses miss around 27% of their inbound calls. – Invoca
• The average business loses $126,360 annually due to unanswered calls. – Local Splash

For context, according to Unicom, 80% business communications still take place over the phone, making this missed call epidemic particularly devastating for revenue generation.

The Voice AI Market Explosion: Numbers Don’t Lie

The voice AI industry isn’t just getting bigger; it’s getting huge. The market data paints a clear picture of where smart businesses are putting their money:

Market Size and Growth Projections

• The global AI voice generator market was valued at $3.0 billion in 2024 and is expected to reach $20.4 billion by 2030, with a CAGR of 37.1%. – Markets and Markets
• The AI voice generators market was estimated at $3.5 billion in 2023 and is projected to reach $21,754.8 million by 2030, growing at a CAGR of 29.6%. – Grand View Research

The North American Advantage

North America is leading in voice AI adoption, particularly in financial services, signaling early opportunities for companies ready to implement scalable solutions.

Note

Test your voice agents across real-world scenarios. Book a Demo!

Beyond the Demo: Why 99.7% Reliability Matters?

Edge case management is what makes some voice AI systems work well and others look good but not work in real life. It’s not just a matter of numbers; the difference between a system that works 90% of the time and one that works 99.7% of the time is the difference between a business tool and a business liability.

Real-World Complexity Voice AI Must Handle

Generic solutions fail in the real world. Only AI that navigates nuanced, multi-step interactions across industries delivers true value.

Every business faces unique scenarios that generic solutions can’t address:

Restaurant Challenges:

• I need a table for eight people, but two of them are in wheelchairs and we can’t eat nuts.
• Complex dietary restrictions combined with availability constraints.
• Handling cancellations during peak dinner rush.

Healthcare Scheduling:

• Insurance verification while scheduling appointments.
• Managing emergency vs. routine appointment priorities.
• Handling sensitive medical information with proper privacy protocols.

Professional Services:

• Multi-location businesses with different service offerings.
• Complex project scoping conversations that require nuanced understanding.
• Managing consultant availability across time zones.

The Economic Impact: ROI That Makes CFOs Happy

The benefits of using voice AI go far beyond just answering calls. Smart businesses are getting more than one benefit in many areas of their operations.

Direct Cost Savings

Traditional reception costs are steep compared to scalable AI solutions.

Traditional Reception Costs:

• Average receptionist salary: $35,000-$45,000 annually. – Salary
• Benefits and overhead: Additional 30-40% of salary. – U.S. Bureau of Labor Statistics
• Training and turnover costs: Industry average turnover every 4 months. – UKG Workforce Institute
• Total annual cost per reception position: $50,000-$70,000.

Voice AI Agent Costs:

• Annual subscription: $3,000-$12,000 depending on call volume.
• Setup and customization: $2,000-$8,000 one-time.
• Ongoing optimization: $1,000-$3,000 annually.
• Total first-year cost: $6,000-$23,000.

Revenue Generation Impact

Retailers that use Voice AI as part of their customer service strategies are seeing more sales, more customer engagement, and more brand loyalty.

The mathematics are compelling:

• If your business averages 50 inbound calls daily.
• With a 40% miss rate, you’re missing 20 calls daily.
• At an average revenue potential of $200 per call.
• You’re losing $4,000 in potential revenue daily.
• Annual missed revenue: $1.46 million.

A voice AI agent that captures even 70% of those missed opportunities generates an additional $1.02 million annually while costing less than $25,000 to implement and maintain.

What Success Actually Looks Like In Implementation?

It’s not a matter of whether to use voice AI; it’s a matter of how to do it in a smart way.

The Best Mix: People and AI

The main point is that voice AI doesn’t take the place of human judgement; it makes people better at what they do.

Effective Division of Labour:

AI takes care of scheduling appointments, answering basic questions, gathering information, and doing initial triage. People handle solving hard problems, dealing with emotions, building high-value relationships, and negotiating with subtlety.

Industry-Specific Applications

Each industry has distinct workflows, and voice AI adapts to those nuances.

Healthcare Practices:

• Scheduling and rescheduling appointments 24/7.
• Pre-verification of insurance.
• Requests for prescription refills.
• Protocols for emergency triage.

Professional Services:

• Lead qualification and setting up the first meeting.
• Questions about services and basic pricing information.
• Collecting documents and getting new clients started.
• Setting up a follow-up appointment.

Retail and eCommerce:

• Questions about the status of an order.
• Processing returns and exchanges.
• Information about where to find products and stores.
• Routing for customer support escalation.

Window of Opportunity for Competitive Advantage is Closing

Business technologies that change things a lot tend to follow a predictable adoption curve. We’ve seen it with social media marketing, mobile apps, websites, and now voice AI.

The Early Adopter’s Edge

In 2025, companies that use voice AI will have several advantages over their competitors:

• Market Positioning: Being the first to come up with new ways to improve the customer experience.
• Cost Structure: Lower costs for putting things into place than what the market will charge in the future.
• Learning Curve: Time to improve and optimise systems before they become standard.
• Talent Acquisition: Get access to specialised voice AI talent before the competition heats up.

Signs That You’re Falling Behind

These red flags indicate your competitors are pulling ahead.

• Competitors saying “available 24/7” when you’re not.
• Customers are unhappy with how hard it is to reach them by phone.
• Staff members are overwhelmed with routine phone tasks during busy times.
• Not being able to answer calls during lunch breaks, meetings, or after work.
• Customers asking, “Do you have a system for booking automatically?”

Critical Testing Phase: Ensuring Voice AI Reliability

Before using any voice AI solution to talk to real customers, it needs to be thoroughly tested. This is where specialised testing platforms prove to be invaluable.

LambdaTest Agent-to-Agent Testing is the best way to check voice AI. This platform enables automated testing of phone-based AI agents by simulating realistic caller scenarios:

• Automated Call Testing: Run hundreds of test calls to validate AI response accuracy across different scenarios.
• Edge Case Validation: Test complex customer situations that could break standard AI responses.
• Performance Under Load: Simulate high call volumes to ensure your voice AI maintains quality during peak times.
• Integration Testing: Verify seamless handoffs between AI agents and human agents.
• Multi-scenario Testing: Test appointment booking, complaint handling, sales inquiries, and technical support flows.

This testing phase is crucial because the difference between a demo that works 9 times out of 10 and a production system that works 997 times out of 1000 lies in comprehensive edge case testing. LambdaTest Agent to Agent Testing platform ensures your voice AI is production-ready before it ever answers a real customer call.

To get started, refer to this Agent to Agent Testing guide.

The 2025 Business Reality Check

Voice AI isn’t just a technology trend; it’s a fundamental shift in how businesses operate.

Questions Every Business Owner Must Answer

These five questions determine whether your customer experience is future-ready:

• Customer Accessibility: Can your customers reach you 24/7, or are you limited by human availability?
• Scalability: When your business grows 50%, can your phone handling capacity scale proportionally without linear cost increases?
• Consistency: Does every caller receive the same quality of service regardless of when they call or which staff member is available?
• Data Collection: Are you capturing and analysing every customer interaction to improve service delivery?
• Competitive Response: When your competitors deploy voice AI, how will you differentiate your customer experience?

Beyond Customer Service: The Broader Business Impact

Voice AI implementation creates ripple effects throughout business operations that extend far beyond answering phones.

Operational Intelligence:

Every voice interaction generates valuable data:

• Common customer pain points and questions.
• Peak call time patterns for staffing optimization.
• Geographic distribution of inquiries.
• Service improvement opportunities based on conversation analysis.

Staff Empowerment:

When routine calls are handled by AI, human staff can focus on:

• Complex problem-solving that requires creativity.
• Building deeper customer relationships.
• Strategic business development activities.
• Skills development and professional growth.

Brand Differentiation:

Voice is one of the most powerful unlocks for AI application companies. As models improve, AI voice will become the wedge, not the product.

Businesses using voice AI can differentiate through:

• Consistent brand personality in every interaction.
• Multilingual customer support without hiring multilingual staff.
• 24/7 availability that matches customer expectations.
• Seamless integration between voice and digital touchpoints.

The Technology Behind the Magic

Understanding the technical capabilities helps businesses make informed implementation decisions.

Voice AI Capabilities (2025):

• Natural language processing that handles conversational nuances.
• Multi-turn conversation management for complex inquiries.
• Real-time integration with business databases and systems.
• Emotion recognition and appropriate response modulation.
• Seamless handoff to human agents when necessary.
• Advanced sentiment analysis for proactive customer service.
• Predictive conversation routing based on customer history.
• Multi-language real-time translation.
• Integration with IoT devices for comprehensive customer context.
• Advanced voice synthesis for personalized brand voices.

Common Implementation Pitfalls to Avoid

Learning from others’ mistakes accelerates successful implementation.

Technical Pitfalls:

• Insufficient Training Data: Failing to provide enough conversation examples for AI learning.
• Poor Integration Planning: Not considering how voice AI connects with existing business systems.
• Inadequate Testing: Launching without comprehensive testing across various customer scenarios.

Strategic Pitfalls:

• Over-Automation: Trying to automate complex scenarios that require human judgment.
• Under-Communication: Failing to inform customers about new AI-assisted service options.
• Neglecting Staff Training: Not preparing human staff for effective AI collaboration.

Operational Pitfalls:

• Ignoring Analytics: Not monitoring and optimizing AI performance based on real usage data.
• Static Implementation: Setting up the system once and never iterating or improving.
• Brand Misalignment: Using generic AI voices that don’t match company personality.

The Future Is Voice: Preparing for What’s Next

The voice AI revolution is just beginning. Businesses that establish strong foundations now will be best positioned for upcoming innovations.

2025-2027 Predictions:

• Voice AI will become the primary customer service interface for 60%+ of businesses.
• Integration with augmented reality will create immersive customer service experiences.
• Predictive voice AI will proactively reach out to customers before they call with problems.
• Voice commerce will enable customers to make purchases entirely through conversation.

Preparing Your Business:

• Start with Core Use Cases: Focus on the 20% of scenarios that handle 80% of your calls.
• Invest in Data Quality: Clean, organized customer data enables more sophisticated AI capabilities.
• Build AI-Friendly Processes: Design business workflows that accommodate both human and AI interaction.
• Develop AI Governance: Establish policies for AI decision-making and human oversight.

Conclusion: The Choice Is Clear, The Time Is Now

There is a lot of proof. Voice AI isn’t something that will happen in the future; it’s something that businesses need to do right now. The companies that succeed in the next ten years will be those that recognize this period as their equivalent of the website boom of the 2020s. Your competitors get stronger every day you wait.

Not only do you lose money when you miss a call, but you also lose a customer who might never give you another chance. Every routine phone task that your staff does is an opportunity cost that grows every day. The question isn’t if voice AI will become common in your field. The question is if you’ll be in charge of the change or if you’ll be trying to catch up.

Your Next Steps are Clear:

• Check how well your phone is working right now – how many calls are you missing? What effect does it have on revenue?
• Figure out how much money you could make by using the framework to understand your own business case.
• Pick your implementation partner, look into voice AI platforms that meet your business needs and the needs of your industry.
• Start small and think big. Start with the things you do the most and grow from there.
• Measure and optimise; use data to make your voice AI work better all the time.

Organizations that view voice AI as the equivalent of a new website will dominate their markets. The question is, will you be one of them?

Ready to join the voice AI revolution? The future of customer service is calling literally. Make sure you’re there to answer.

Frequently Asked Questions (FAQs)

How does a voice AI agent work?

A voice AI agent listens to spoken input, interprets meaning using natural language processing, and responds using text-to-speech. It continuously learns from interactions to improve accuracy. This allows businesses to automate calls, answer queries instantly, and deliver consistent customer experiences efficiently.

Can voice AI agents handle multiple languages?

Yes, modern voice AI agents support multiple languages and dialects. You configure language settings and train models to understand accents and context. This enables global customer support without extra staff, improving accessibility and keeping conversations natural and effective across markets.

What are the benefits of voice AI agents?

Voice AI agents save time, reduce operational costs, and provide consistent customer service. They handle repetitive tasks, offer real-time responses, and deliver valuable analytics, allowing businesses to improve efficiency, scale support, and enhance the overall customer experience effectively.

Are voice AI agents secure?

Security depends on the platform and implementation. Reputable providers offer encrypted communication, secure data storage, and compliance with privacy regulations. Always monitor access controls and data handling practices to ensure sensitive customer information remains protected while using AI voice solutions safely.

How can I integrate a voice AI agent with my systems?

Integration usually involves APIs, webhooks, or SDKs provided by the AI platform. You connect the agent to CRM, support software, or databases so it can access relevant information. Proper integration ensures smooth automated workflows and accurate, real-time responses for users.

Which industries benefit most from voice AI agents?

Industries like customer support, healthcare, banking, and retail gain the most. Agents can handle appointments, answer inquiries, provide updates, and resolve common issues. Businesses save staff time, maintain consistency, and deliver 24/7 service that meets customer expectations effectively.

How do I train a voice AI agent?

You start by feeding it conversation data, defining intents, and mapping responses. Continuous testing and real-user feedback refine accuracy. Regular updates ensure it understands diverse phrasing, accents, and edge cases, keeping interactions smooth, natural, and highly reliable for customer engagement.

What hardware or software is needed for a voice AI agent?

You need a cloud AI platform or on-premises server, microphones for voice input, and speakers for output. Software includes speech recognition, natural language processing, and text-to-speech modules. Proper configuration ensures the agent can handle multiple calls efficiently and provide consistent performance.

Can a voice AI agent handle complex customer queries?

Yes, with proper training and integration with knowledge bases or CRMs, voice AI agents can handle complex queries. They may escalate certain cases to humans when necessary, but routine multi-step tasks can be managed autonomously, improving response times and customer satisfaction effectively.

How do I get a phone number for my voice AI agent?

You obtain a number from cloud communication providers like Twilio or Plivo. They offer local, toll-free, or international options. Once linked to your agent, customers can call directly, enabling immediate interaction and professional communication without additional infrastructure or staff requirements.

Citations

• Voice AI in Firms: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5395709




Co-Author: Sai Krishna

Sai Krishna is a Director of Engineering at LambdaTest. As an active contributor to Appium and a member of the Appium organization, he is deeply involved in the open-source community. He is passionate about innovative thinking and love to contribute to open-source technologies. Additionally, he is a blogger, community builder, mentor, international speaker, and conference organizer.

]]> https://www.lambdatest.com/blog/how-to-screenshot-a-whole-page/ Tue, 30 Sep 2025 05:09:49 +0000 https://www.lambdatest.com/blog/?p=92442

Capturing a full-page screenshot has become a fundamental skill for developers and testers. Whether you need to document a bug, create tutorials, archive web pages, or present complete web layouts, a standard screenshot often falls short because it only captures what’s visible on your screen.

Learning how to screenshot a whole page allows you to capture entire web pages, including areas that require scrolling, ensuring that no detail is missed.

Overview

If you’re wondering how to screenshot a whole page, different browsers and tools make the process simple:

Why taking a screenshot of a whole page work?

• In Google Chrome: Use Developer Tools (Ctrl+Shift+P → “Capture full-size screenshot”) or extensions like GoFullPage.
• In Firefox: Take advantage of the built-in screenshot option to capture the entire page without third-party tools.
• Using Browser Extensions: Tools like Fireshot and Nimbus work across browsers for quick scrolling captures.
• For Automation: Frameworks such as Selenium or Puppeteer can automate full-page screenshots during testing.

This ensures you can capture entire webpages, whether for QA, design reviews, or documentation, without missing critical details below the fold.

TABLE OF CONTENTS

• Why Full-Page Screenshots Matter?
• How to Screenshot a Whole Page in Google Chrome?
  
• How to Take a Full-Page Screenshot in Mozilla Firefox?
• How to Take a Full-Page Screenshot in Microsoft Edge?
• How to Take a Full-Page Screenshot in Safari on Mac?
• How to Take a Full-Page Screenshot on Mobile Devices?
• How to Take a Full-Page Screenshot Using LambdaTest?
• Compare Screenshots with SmartUI for Visual Regression Testing
• Using Browser Extensions Across Platforms
• Advanced Techniques and Tools for Developers and QA
• Comparison of Methods and Tools
• Best Practices for Screenshot Testing High-Quality
• Frequently Asked Questions (FAQs)

Why Full-Page Screenshots Matter?

A traditional screenshot captures only what’s visible on the screen, but most webpages today are scrollable and content-rich. Knowing how to screenshot a whole page ensures that no important detail below the fold is missed. Full-page screenshots are essential for several reasons:

• Bug Reporting

Developers can see the complete context of an issue, including hidden or scrollable elements. Full-page screenshots help identify layout bugs, broken components, and UI inconsistencies efficiently during debugging or QA review.

• Design Review

QA teams and designers can verify layout consistency, color schemes, and responsiveness across devices. Full-page screenshots allow for accurate visual comparisons and ensure that the design matches intended specifications without missing elements.

• Documentation & Tutorials

Creating step-by-step guides or tutorials becomes easier with full-page screenshots. Capturing the entire content ensures that readers follow the instructions accurately without skipping any important sections of the webpage.

• Archiving Web Content

Webpages can be saved for compliance, research, or future reference. Full-page screenshots preserve the complete layout and content, making it easier to review historical versions or maintain records for legal or archival purposes.

Without a full-page screenshot, you risk miscommunication, incomplete documentation, or misrepresentation of your web content.

How to Screenshot a Whole Page in Google Chrome?

Google Chrome provides multiple methods to capture full-page screenshots, whether on desktop or mobile.

Using Developer Tools

1. Open the webpage in Chrome.
2. Press Ctrl + Shift + I (Windows/Linux) or Cmd + Option + I (Mac) to open Developer Tools.
3. Press Ctrl + Shift + P (Windows/Linux) or Cmd + Shift + P (Mac) to open the Command Menu.
4. Type screenshot and select Capture full size screenshot.



5. The screenshot is saved as a PNG file in your downloads folder.

Using Chrome Built-in Options (Mobile/Desktop)

• On Chrome for Android, press Power + Volume Down to take a screenshot, then select Capture more to extend to the full page.
• On Chrome for Chromebook, you can use the Developer Tools method above.

How to Take a Full-Page Screenshot in Mozilla Firefox?

Firefox has a robust built-in screenshot tool, which makes full-page capture straightforward.

Built-in Screenshot Tool

• Open Firefox and navigate to your page.
• Click the three-line menu → More tools → Web Developer Tools.
• Click on the camera icon to save the full page screenshot.



Using Firefox Extensions

• Awesome Screenshot: Capture, annotate, and share full-page screenshots.
• Fireshot: Capture full page, selected region, or visible screen; export to PDF.

How to Take a Full-Page Screenshot in Microsoft Edge?

Microsoft Edge provides an integrated Web Capture tool:

1. Open Edge and navigate to the webpage.
2. Press Ctrl + Shift + S to open Web Capture.
3. Select Capture full page.



The screenshot opens in a new tab and can be saved or annotated.

How to Take a Full-Page Screenshot in Safari on Mac?

Using Developer Tools

1. Open Safari and load the desired page.
2. Press Cmd + Option + I to open Developer Tools.
3. Click Elements → right-click → Capture Screenshot.
4. Screenshot saves as PNG.

Using Safari on iPhone (iOS)

1. Open Safari and navigate to your page.
2. Press Side Button + Volume Up.
3. Tap Full Page in the screenshot preview.
4. Save as PDF via Done > Save PDF to Files.

How to Take a Full-Page Screenshot on Mobile Devices?

iPhone (iOS)

• Use Safari as mentioned above.
• Third-party apps like Paparazzi or Stitch can create combined screenshots from multiple captures.

Android

• Press Power + Volume Down → tap Capture more → save screenshot.
• For older devices, third-party apps like LongShot for Long Screenshot or Stitch & Share help capture full pages.

How to Take a Full-Page Screenshot Using LambdaTest?

LambdaTest provides a seamless way for developers and testers to capture full-page automated screenshots across 3000+ real browsers, operating systems, and device combinations. This ensures accurate validation of user interfaces under diverse test environments without local setup overhead.

Here are the steps to capture a full-page screenshot on LambdaTest:

1. Sign in to your LambdaTest dashboard. If you do not have an account, register for free access to cross-browser testing features.
2. From the LambdaTest dashboard, navigate to the More Tools > Screenshot.



3. Provide the webpage URL you want to test. Choose from a wide range of browsers, versions, devices and operating systems.



4. Click on CAPTURE. LambdaTest will launch your webpage on selected environments and automatically capture full-page screenshots.



Screenshots are displayed within the dashboard. You can view them online, share links with your team, or download them in bulk for documentation or defect reporting.

In screenshot testing, simply capturing a screenshot is not enough. Comparing two screenshots, one from the baseline and one from the latest build, helps testers quickly detect visual regressions, cross-browser inconsistencies, and unintended UI changes, ensuring the application’s interface remains consistent and reliable. This is where LambdaTest SmartUI comes into the picture.

Compare Screenshots with SmartUI for Visual Regression Testing

Capturing full-page screenshots is only the first step in ensuring application quality. To identify subtle UI inconsistencies, testers often require screenshot comparison capabilities.

LambdaTest’s SmartUI feature enables automated visual testing by comparing new screenshots against established baselines, ensuring that unintended changes in the user interface are quickly detected.

Features:

• SmartUI SDK: Using the SmartUI SDK config options, you can seamlessly capture full-page screenshots for both web applications and mobile pages across different environments.
• Baseline Image Creation: Testers can set an initial screenshot as the baseline. Future test runs automatically compare against this baseline to highlight even pixel-level deviations.
• Automated Visual Comparisons: SmartUI compares current screenshots with stored baselines, eliminating the need for manual image reviews. Differences are flagged with visual highlights for faster debugging.
• Cross-Browser Consistency Checks: Teams can validate whether the UI looks consistent across multiple browsers, versions, and devices, identifying rendering issues early in the cycle.
• Seamless Integration with Workflows: SmartUI integrates with CI/CD pipelines, making visual regression checks part of automated builds. This reduces release risks and supports continuous testing practices.
• Actionable Reporting: Differences are presented with clear, side-by-side comparison views. This allows testers and developers to make informed decisions on whether UI changes are expected or regressions.

To get started, check out this guide on visual regression tests with SmartUI

Using Browser Extensions Across Platforms

Extensions simplify full-page capture across browsers:

	Extension	Browser	Features
	GoFullPage	Chrome	Full-page capture
	Awesome Screenshot	Chrome, Firefox, Edge	Capture, annotate
	Nimbus Screenshot	Chrome, Firefox, Edge	Capture, edit, export PDF
	Fireshot	Chrome, Firefox, Edge	Full-page, edit, PDF export

Tip: Extensions often add extra features like annotations, PDF export, or batch capture, which are helpful for QA and documentation.

Advanced Techniques and Tools for Developers and QA

For technical users, advanced tools can improve productivity. These methods go beyond basic screenshots, offering automation, cross-browser validation, and integration with CI/CD pipelines to streamline testing and reporting.

• ShareX (Windows): Open-source tool, supports scrolling capture, GIFs, and annotations.
• Snagit: Paid tool for editing, annotations, and batch capture.
• Automation Tools: Use Selenium or Puppeteer scripts to automate full-page screenshot capture for testing workflows.

Tip: For QA, using automation tools saves time when capturing multiple pages across environments or browser versions.

Comparison of Methods and Tools

Platform/Method	Ease of Use	Advanced Features	Output Format	Best For
Chrome DevTools	Medium	Developer-focused	PNG	Pixel-perfect screenshots
Firefox Built-in	Easy	Basic editing	PNG	Quick capture with full page
Edge Web Capture	Easy	Basic editing	PNG	Enterprise documentation
Safari Mac	Medium	Limited	PNG	Designers, developers
Safari iOS	Easy	Save as PDF	PDF	Archiving and sharing
GoFullPage	Easy	Annotation, PDF	PNG, PDF	Non-technical users
Awesome Screenshot	Easy	Annotation, sharing	PNG, PDF	QA, content creators
ShareX	Medium	Advanced, automation	PNG, GIF	Developers & testers
Snagit	Easy	Editing, batch capture	PNG, PDF	Professional documentation

Best Practices for Screenshot Testing High-Quality

Taking a full-page screenshot isn’t just about pressing the right keys or using the correct tool. To make your captures useful, professional, and impactful, you need to follow certain best practices. These ensure your screenshots maintain clarity, relevance, and consistency across projects.

• Use High Resolution
  Always capture screenshots in the original resolution of your screen or browser window. High-resolution images ensure that text remains sharp and design details are preserved, making your screenshot usable for bug reporting, design reviews, or documentation purposes without losing critical clarity.
• Remove Clutter
  Before capturing a full-page screenshot, remove any distractions such as unnecessary browser tabs, app notifications, or background apps. Cleaner screenshots provide focus and avoid confusion, ensuring that reviewers or stakeholders only see relevant information. This also minimizes data leakage risks when sharing images externally.
• Annotate Carefully

  Annotations are powerful for highlighting specific areas in your screenshot, but moderation is key. Use boxes, arrows, or callouts sparingly to emphasize important details while avoiding visual overload. A clutter-free annotated screenshot communicates your intent clearly, making it easier for teams to understand the issue at a glance.

• Choose the Correct Format

  Different use cases demand different file formats. Opt for PNG when you need pixel-perfect quality for UI elements or design reviews. Use PDF when you’re archiving, creating tutorials, or handling multi-page documentation. The right format ensures compatibility, readability, and ease of sharing across devices and platforms.

• Automate Where Possible
  When you need to take repetitive full-page screenshots, leverage automation tools like ShareX, Puppeteer, or Selenium. Automation not only saves time but also ensures consistency in output. This is especially useful in QA workflows, regression testing, or capturing bulk screenshots across multiple pages or environments.
• Cross-Browser Testing
  Full-page screenshots can look different depending on the browser used. Always test and capture screenshots in multiple browsers—Chrome, Firefox, Safari, and Edge—to ensure visual consistency. This practice helps developers and testers identify rendering issues early and guarantees that the user experience remains seamless across platforms and devices.

Conclusion

Full-page screenshots are essential for developers, testers, QA engineers, and content creators. With built-in browser tools, mobile features, browser extensions, and advanced tools, you can capture complete web pages efficiently. Following best practices ensures high-quality, professional results that are ready for documentation, tutorials, or testing.

For technical users, combining automation scripts with extensions streamlines full-page capture across multiple pages and browsers. By mastering these techniques, you ensure your web documentation is complete, professional, and consistent, improving both workflow efficiency and presentation quality.

Frequently Asked Questions (FAQs)

Can I capture infinite scrolling pages?

Yes. Tools like Fireshot, ShareX, and automated scripts in Puppeteer or Selenium can capture infinite scrolling pages by dynamically loading and stitching content. This ensures that even continuously loading feeds or timelines are fully recorded.

Can I include the mouse pointer in screenshots?

Yes, many advanced screenshot tools allow you to capture the mouse pointer. Snagit and ShareX provide this option, which is particularly useful for creating training materials, tutorials, or product walkthroughs. Including the cursor visually guides viewers, making it easier for them to follow navigation steps, click actions, and interactions within the application or browser.

Are there free tools for full-page screenshots?

Absolutely. Free tools like GoFullPage (Chrome extension), Firefox’s built-in screenshot utility, and ShareX (for Windows) provide excellent full-page capture capabilities. They don’t require premium subscriptions and often include features like scrolling capture, annotation, or direct export. While free tools may lack some advanced editing options compared to paid ones, they are reliable for most testing, documentation, and design purposes.

Can I automate full-page screenshots for testing?

Yes, automation is one of the best ways to capture full-page screenshots at scale. Frameworks like Selenium and Puppeteer can be scripted to take screenshots across multiple devices, browsers, and environments, ensuring consistency in regression testing. Tools like ShareX also allow automation with hotkeys and scripting support. Automation reduces manual effort, speeds up QA cycles, and helps maintain documentation quality during continuous integration (CI/CD) processes.

Which format is best for sharing screenshots?

The choice of format depends on your use case. PNG is ideal when you need high-quality, pixel-perfect clarity for design reviews, bug reporting, or UI comparisons. JPEG may be used for web sharing when file size matters, though it sacrifices some quality. PDF is best suited for archiving entire web pages or sharing multi-page documents in a professional format. By choosing the right format, you balance clarity, compatibility, and file size.

Do full-page screenshots affect website performance?

Taking a screenshot itself does not affect the performance of a live website, as it only interacts with your local browser. However, system resources like CPU and RAM may be slightly impacted during capture, especially for pages that are very long or include heavy animations, infinite scrolling, or dynamic elements. In extreme cases, this could cause lag or increase memory usage temporarily, but it does not harm the website or alter its performance for other users.

How do I screenshot a full page on mobile devices?

On iOS devices, Safari provides a built-in option to capture full-page screenshots and save them as a PDF. On Android devices, many manufacturers include scrolling screenshot features directly in the OS, allowing you to capture extended pages without third-party tools. For more advanced use cases, apps like LongShot or Stitch & Share can help you capture and stitch longer web pages or conversations. This makes it easy to capture mobile layouts for QA testing or design validation.

Can I edit full-page screenshots after capturing?

Yes, most screenshot tools provide editing and annotation features. Applications like Snagit, Fireshot, and Photoshop allow you to crop sections, resize, highlight elements, blur sensitive data, and add text or arrows. This makes it easy to prepare professional, share-ready screenshots for reporting, client presentations, or tutorials. Editing also ensures clarity, helping teams focus only on the areas that matter in testing or debugging.

Are browser extensions safe for taking screenshots?

Many popular screenshot extensions like GoFullPage, Nimbus, and Fireshot are safe and widely used. However, you should always review extension permissions before installing. Some less reputable extensions may request unnecessary access to browsing history or data, which could be a privacy concern. Stick to trusted extensions from official stores, check user reviews, and update them regularly to ensure security and compatibility with your browser.

What’s the difference between full-page screenshots and viewport screenshots?

A viewport screenshot captures only the content currently visible in your browser window, essentially what you see without scrolling. A full-page screenshot, however, scrolls automatically and stitches together the entire webpage into a single image or file. This allows you to document complete web layouts, test responsive designs, or archive entire web pages, which is especially useful in web development, QA, and design workflows.

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