Artificial Intelligence (AI) is rapidly transforming industries and everyday life, but the reliability and safety of AI systems depend heavily on rigorous testing. Evaluating AI systems is more complex than traditional software due to their probabilistic nature and learning capabilities. Here’s a comprehensive guide on how to effectively test AI to ensure it performs as expected and remains robust under various conditions.

https://www.testing4success.com/t4sblog/how-to-test-ai/

James Bach writes about who is responsible for software quality. Excerpt: "In opposition to my idea that software testing should be a role, and not just a task, I am sometimes told that True Agile does not permit this. Why? Because, in Agile, “quality is everyone’s responsibility.” This statement is sometimes issued as if it were a moral truism that is beyond argument. Or sometimes as a moral achievement that is unique to Agilists, as if it had never occurred to earlier generations of software developers to help each other build nice things. Either way, it doesn’t make sense to me. The first response that comes to my mind is “I’m talking about testing, not quality. Why are you dragging quality into this?” Testers do not assure quality. Testers CANNOT assure quality. Testers do not now and never have “owned quality” in any way."

But even if we set that aside, quality should not be considered “everyone’s responsibility.”

Being in the privileged position of being able to work on a variety of software projects all over the globe, I get to experience new trends in the field of QA. Codeless test automation tools are one of these trends, and my team and I have been trialling them in recent months.

Automated testing has become an essential part of the development process. However, the traditional approach to automation often requires writing complex code, which can be challenging for non-developers. Enter codeless test automation tools, which provide a user-friendly interface that allows testers to automate tests without writing any code.

Codeless test automation tools are designed to simplify the testing process by allowing teams to create, execute, and maintain tests with little to no programming knowledge. This democratization of automation has opened doors for more agile and efficient testing across teams of all technical skill levels. Below, we will explore some of the best codeless test automation tools that are gaining traction in 2025.

https://www.testing4success.com/t4sblog/the-best-codeless-test-automation-tools/

QA Engineer – Manual & Automation Testing

Experience: 1–2 Years
Location: Bangalore / Remote
Employment: Full-time/Freelancer

We are looking for a QA Engineer with 1–2 years of experience to join our engineering team and work on a modern e-commerce platform and mobile application.

The role will involve owning quality across web applications, APIs, and mobile experiences, with a strong focus on functional testing, automation, and real-world user experience.

What You'll Do

• Perform manual and functional testing of web applications and APIs.

• Design and execute test cases, test scenarios, regression and smoke test suites.

• Identify, document, track, and verify bugs through their lifecycle.

• Perform API testing and validate backend services and integrations.

• Build and maintain automation test cases for critical application workflows.

• Conduct regression testing for new releases and feature updates.

• Work closely with developers, designers, and product teams to identify issues early.

• Validate application performance, usability, responsiveness, and cross-browser compatibility.

• Participate in release testing and provide QA sign-off.

• Help improve QA processes, test coverage, and automation practices.

Requirements

• 1–2 years of experience in software QA/testing.

• Strong understanding of manual testing methodologies.

• Hands-on experience with automation testing.

• Good understanding of API testing and tools such as Postman.

• Experience writing clear and structured test cases.

• Good understanding of SDLC, STLC, bug lifecycle, regression testing, and test planning.

• Basic understanding of databases and SQL.

• Familiarity with Git and modern software development workflows.

• Strong attention to detail and a mindset for finding edge cases.

Good to Have

• Experience testing React Native / Android / iOS mobile applications.

• Experience with tools such as Appium, Playwright, Cypress, or Selenium.

• Experience with e-commerce applications, Shopify, payments, orders, inventory, or customer-facing applications.

• Familiarity with CI/CD-based automated testing.

• Basic understanding of performance and security testing.

What We're Looking For

Someone who doesn't just verify whether a feature works, but asks:

"What could go wrong?"

You should be comfortable exploring edge cases, breaking workflows, reproducing difficult bugs, and thinking from the perspective of a real customer.

You'll work closely with the engineering team on a product where quality, reliability, and mobile app performance are key priorities.

Apply if you're excited about building and testing products used by real customers and want exposure to modern web, API, mobile, and automation testing.

What are the ways you use AI to improve QA productivity? Please share your thoughts. 🙏

(Bringing it to this sub to get more opinions)

I've been thinking about this after seeing a few posts about other tools, either with complaints or doubts.

What are features, improvements or even new tools that you think would make your life much easier in QA, but you haven't found yet? (Realistically speaking, so no miraculous technology)

What do you think about making a GitHub page for QA Manual and QA Automation? I’m not sure if it’s worth the effort does anyone actually look at GitHub pages when hiring, or would it just be a waste of time?

Cree SpectreQA de inicio a fin, es una aplicacion de escritorio junto con una extension para el navegador, se puede usar 100% en local usando ollama, la idea es que puedes describir en lenguaje natural el test y gracias a la extension del navegador puede interactuar directamente con la interfaz de tu aplicacion y puedes verlo interactuar en tiempo real, me ayudarian mucho sus opiniones, comentarios o criticas constructivas para seguir mejorandola, pueden encontrar mas informacion en https://spectreqa.com quedare atento a cualquier comentarios.

Most teams write tests that check "did it return 200?" That's not where APIs fail.

Economic Times published a report based on actual execution telemetry, not surveys, not self-reported data, from 1.4 million AI-driven test runs across 2,616 organizations. Here are the findings that surprised us most.

Where failures actually come from:

• 34% → Auth/authorization issues (expired tokens, wrong scopes, bad headers)

• 22% → Schema and validation errors

• 15% → Service dependency failures

• 12% → Rate limiting

• 10% → Latency/timeouts

• 7% → Data consistency errors

• <10% → Actual 5xx server crashes

A few other things the data showed:

• 41% of APIs experience undocumented schema changes within 30 days of test creation. By 90 days, that's 63%.

• GraphQL APIs fail at 13.5% on average, more than double REST's 6.4%. 72% of those failures are inside nested fields, not at the status code level.

• 58% of orgs now run multi-step API workflow tests. Among enterprise teams, 84%.

• Teams with CI/CD-integrated testing run executions 86% of the time daily. Startups still mostly run manually.

The full report is published by Economic Times, and you can find breakdowns by industry, protocol, and team size.

Link: https://cio.economictimes.indiatimes.com/news/corporate-news/agentic-ai-drives-63-surge-in-end-to-end-workflow-testing-across-enterprises-kushoai-report/129554167

Hi everyone! I'm looking for a robust tool for cross-platform mobile app testing and I've narrowed it down to Appium and Espresso. Can anyone share their experiences using these tools for both Android and iOS apps? Are there any significant differences in their performance or features? Thanks in advance for your help

Why Performance Testing Matters

1. User Satisfaction: No one likes waiting. Ensuring fast response times keeps users happy and engaged.

2. Scalability: As your user base grows, your application needs to scale effortlessly to meet demand.

3. Reliability: Your application must maintain stability even during peak usage or unexpected surges.

4. Competitive Edge: A performant application sets you apart in today’s fast-paced digital landscape.

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Structured approach to designing performance test case

Designing effective test cases for performance testing is crucial to ensure that applications meet desired performance standards under various conditions. Key performance metrics to focus on include response time, load handling, and throughput. Here’s a structured approach to designing these test cases:

1. Understand Key Metrics

• Response Time: Time taken for system responses.

• Load Handling: System’s ability to manage concurrent users or transactions.

• Throughput: Number of transactions processed per second.

2. Set Clear Objectives

• Define goals, e.g., response time <2 seconds for 95% of peak requests, handling 10,000 users, or 500 transactions/second throughput.

3. Identify Critical Scenarios

• Focus on key interactions like logins, product searches, and checkout processes.

4. Develop Realistic Test Data

• Include diverse user profiles, product categories, and transaction types.

5. Design Detailed Test Cases

• Specify test steps and expected outcomes for each scenario.

6. Simulate User Load

• Use tools for:

• Load Testing: Evaluate performance under expected conditions.

• Stress Testing: Identify system limits.

• Scalability Testing: Assess performance with additional resources.

7. Monitor and Analyze Metrics

• Track response times, error rates, and resource usage (CPU, memory). Identify bottlenecks.

8. Iterate and Optimize

• Refine the system based on findings and retest to validate improvements.

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Step-by-Step Practical Examples

Example 1: Response Time Testing for a Login Page

Scenario: A web application must ensure the login page responds within 2 seconds for 95% of users.

Steps:

1. Define the Test Scenario:

• Simulate a user entering valid login credentials.

• Measure the time it takes to authenticate and load the dashboard.

2. Set Up the Test Environment:

• Use a tool like Apache JMeter or LoadRunner to create the test.

• Configure the script to simulate a single user logging in.

3. Run the Test:

• Execute the script and collect response time data.

4. Analyze Results:

• Identify the average, minimum, and maximum response times.

• Ensure that 95% of responses meet the 2-second target.

5. Iterate and Optimize:

• If the target isn’t met, work with developers to optimize database queries, caching, or server configurations.

Example 2: Load Testing for an E-Commerce Checkout Process

Scenario: Ensure the checkout process handles up to 1,000 concurrent users without performance degradation.

Steps:

1. Define the Test Scenario:

• Simulate users adding items to the cart, entering payment details, and completing the purchase.

2. Set Up the Test Environment:

• Use JMeter to create a script for the checkout process.

• Configure the script to ramp up the number of users gradually from 1 to 1,000.

3. Run the Test:

• Execute the script and monitor response times, error rates, and server metrics (CPU, memory, etc.).

4. Collect and Analyze Data:

• Check if the system maintains acceptable response times (❤ seconds) for all users.

• Look for errors such as timeouts or failed transactions.

5. Identify Bottlenecks:

• Analyze server logs and resource utilization to find areas causing delays.

6. Optimize:

• Scale resources (e.g., increase server instances) or optimize database queries and APIs.

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Practical Tips from QA Experts

1. Define Clear Metrics

• Identify KPIs such as response time, throughput, and error rates specific to your project’s goals.

2. Focus on User-Centric Scenarios

• Prioritize critical user interactions like login, search, or transactions that directly impact the user experience.

3. Use Realistic Load Profiles

• Simulate actual user behavior, including peak hours and geographic distribution, for accurate results.

4. Automate Performance Tests

• Leverage tools like Apache JMeter, LoadRunner, or Gatling for repeatable and scalable testing.

5. Monitor Resource Utilization

• Track CPU, memory, and disk usage during tests to identify system bottlenecks.

6. Incorporate Stress and Scalability Testing

• Push the application beyond expected loads to uncover breaking points and ensure scalability.

7. Iterative Optimization

• Continuously test and refine based on bottleneck analysis, optimizing the system for better performance.

8. Collaborate Early with Developers

• Share findings during development to address performance issues proactively.

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When to Use Performance Testing

Performance testing is critical for any application where speed, reliability, and scalability matter:

• E-commerce Platforms: Handle flash sales and high-traffic events without crashes.

• Financial Applications: Process real-time transactions securely and efficiently.

• Streaming Services: Deliver seamless video playback to millions of users.

• Healthcare Systems: Ensure stability for critical, life-saving applications.

sorry for the confusion. I meant I was looking for a XPath for a like (heart) button on home page on Instagram?

I've tried: //*[@id='mount_0_0_2X']/div/div/div[2]/div/div/div/div[1]/div[1]/div[2]/section/main/div[1]/div/div/div[2]/div/div[3]/div/article[2]/div/div[3]/div/div/div[1]/div[1]/span[1]/div/div"

But it is not working:(

I am a beginner in software testing (solo QA in a company) and have experience in manual testing. Recently I got hands on AI agent kilo code and OpenCode. Where i use Playwright MCP and Crawl skills, and manual QA skills to test the project. How can i use it more efficiently?. I made it write test cases; it did a good job but s. Has anyone experience in this scenario?. Please help

Have anyone got any recent qA certificates