B S Sathyamoorthy (Sathya)’s Post

A to Z into Agile Software Testing, End to End Process Overview ⭐ Agile Testing Life Cycle: 1) Impact Assessment or Feedback Loop from Customers: feedback from users and stakeholders helps set objectives for the upcoming cycle Ex: In Zomato app, there is an option to add tips for delivery person. Update the tips UI for ease of use by end users. 2) Agile Planning:  Stakeholders collaborate to plan the test process and schedule deliverables Ex: Planning testing strategies for a new software release with input from developers, testers, and project managers 3) Release: Features are reviewed to assess their readiness for deployment, and decisions are made on whether to return to the development phase. Ex: Validating software quality and conducting acceptance or A/B Tests. Note: there can be Prod Green/Blue deployments too or Geolocation wise releases 4) Daily Scrum: Standup calls for discussion of progress, show-stoppers etc. Ex: Testers and developers meet each morning to review tasks completed, identify any blockers, and plan their activities for the day 5) Deployment: Post deployment to prod blue and validating the traffic, logs and general checkup for application health. Monitoring this for 1 or more days then Green deployment can be executed ⭐ Popular Test Types Agile: 1) Exploratory: Ad-hoc testing approach where testers explore the application without predefined test cases to discover defects Ex: Navigating through the application to find usability issues or unexpected behaviors. Why exploratory? : Provides a more dynamic and creative approach to finding defects compared to scripted testing 2) Regression: Validating previous and current behaviour of application with current version of software. Meaning old, new and improved features should be working fine Ex: After adding a new feature, running previous test cases to ensure existing features still work Why Regression? : Ensures that new code changes haven't introduced new defects or broken existing functionalities 3) Acceptance Tests: Fulfilling the acceptance criteria. Ex: End-to-end testing to verify if the software satisfies user stories. Why Acceptance? : Focuses on the user's perspective to determine if the software is acceptable for release. 4) Smoke: Build validation testing, Critical functionality of application is validated Ex: After integrating new code changes, running a smoke test to ensure that the application launches successfully and critical functionalities like login and basic navigation work Why Smoke Tests?: Smoke testing is broad and shallow, focusing on basic functionality to ensure the build is stable enough for more comprehensive tests (Due to 3000 word limit will continue this in next post) -x-x- Crack Test Automation Interviews with Java coding: https://lnkd.in/g5hr9bea Become a SDET and Future SDET Manager + 950+ SDET Interview Prep Q&A Bank: https://lnkd.in/gusymgFi Read my Technical blogs: https://lnkd.in/gCC34Vv2 #japneetsachdeva

Cutting Edge SDETs Agile Test Cycle Review ⭐ Agile Testing Life Cycle: 1) Impact Assessment or Feedback Loop from Customers: feedback from users and stakeholders helps set objectives for the upcoming cycle Ex: In Zomato app, there is an option to add tips for delivery person. Update the tips UI for ease of use by end users. 2) Agile Planning:  Stakeholders collaborate to plan the test process and schedule deliverables Ex: Planning testing strategies for a new software release with input from developers, testers, and project managers 3) Release: Features are reviewed to assess their readiness for deployment, and decisions are made on whether to return to the development phase. Ex: Validating software quality and conducting acceptance or A/B Tests. Note: there can be Prod Green/Blue deployments too or Geolocation wise releases 4) Daily Scrum: Standup calls for discussion of progress, show-stoppers etc. Ex: Testers and developers meet each morning to review tasks completed, identify any blockers, and plan their activities for the day 5) Deployment: Post deployment to prod blue and validating the traffic, logs and general checkup for application health. Monitoring this for 1 or more days then Green deployment can be executed ⭐ Popular Test Types Agile: 1) Exploratory: Ad-hoc testing approach where testers explore the application without predefined test cases to discover defects Ex: Navigating through the application to find usability issues or unexpected behaviors. Why exploratory? : Provides a more dynamic and creative approach to finding defects compared to scripted testing 2) Regression: Validating previous and current behaviour of application with current version of software. Meaning old, new and improved features should be working fine Ex: After adding a new feature, running previous test cases to ensure existing features still work Why Regression? : Ensures that new code changes haven't introduced new defects or broken existing functionalities 3) Acceptance Tests: Fulfilling the acceptance criteria. Ex: End-to-end testing to verify if the software satisfies user stories. Why Acceptance? : Focuses on the user's perspective to determine if the software is acceptable for release. 4) Smoke: Build validation testing, Critical functionality of application is validated Ex: After integrating new code changes, running a smoke test to ensure that the application launches successfully and critical functionalities like login and basic navigation work Why Smoke Tests?: Smoke testing is broad and shallow, focusing on basic functionality to ensure the build is stable enough for more comprehensive tests (Due to 3000 word limit will continue this in next post) -x-x- Crack Test Automation Interviews with Java coding: https://lnkd.in/g5hr9bea Become a SDET and Future SDET Manager + 950+ SDET Interview Prep Q&A Bank: https://lnkd.in/gusymgFi Read my Technical blogs: https://lnkd.in/gCC34Vv2 #japneetsachdeva

🚀 Stop Wasting Time & Money! Master Agile Testing with Shift-Left & Shift-Right Strategies 🚀 Are you tired of costly software bugs and endless testing cycles? Agile testing, using shift-left and shift-right strategies, is your solution! It's like having a superhero team ensuring your software is amazing from the start to finish. What's the Challenge? Traditional testing methods are slow, expensive, and often miss crucial bugs. This leads to delays, frustrated users, and wasted resources. The Opportunity: Agile testing dramatically improves software quality and reduces costs by shifting testing earlier (shift-left) and later (shift-right) in the development process. The Solution: Agile Testing with Shift-Left & Shift-Right This powerful approach involves: 1️⃣ Shift-Left: Testing starts early! Involve testers from the planning stage, using techniques like Test-Driven Development (TDD) and Behavior-Driven Development (BDD) to write automated tests before coding. This catches bugs early, when they're easiest and cheapest to fix. 2️⃣ Shift-Right: Testing continues after deployment! Monitor your software in real-world conditions, using tools like feature toggles, canary releases, and real user monitoring (RUM). This ensures your software performs flawlessly for your users. 3️⃣ Agile Test Pyramid: Focus on unit tests (lots!), then integration tests, and finally, fewer end-to-end tests. This ensures efficient and comprehensive testing. 4️⃣ Continuous Integration/Continuous Delivery (CI/CD): Automate testing and deployment, ensuring rapid feedback and faster releases. 5️⃣ Collaboration: Testers, developers, and stakeholders work together throughout the entire process, sharing feedback and improving quality. Key Benefits: 1️⃣ Reduced Defects: Catch bugs early, saving time and money. (Measure: # of bugs found in production; Frequency: Monthly; Goal: Reduce by 50%) 2️⃣ Faster Releases: Automated testing and CI/CD accelerate delivery. (Measure: Time to market; Frequency: Quarterly; Goal: Reduce by 20%) 3️⃣ Improved User Satisfaction: Thorough testing ensures a high-quality user experience. (Measure: User satisfaction scores; Frequency: Monthly; Goal: Increase by 15%) Tools & Technologies: 1️⃣ TestRail: Test management tool. 2️⃣ Selenium: Automated testing framework. 3️⃣ Jenkins: CI/CD tool. KPIs for Success: 1️⃣ Defect Density: (# of defects / # of lines of code). Unit: Defects/KLOC; Frequency: Monthly; Goal: Reduce by 25%. 2️⃣ Test Automation Rate: (% of tests automated). Unit: Percentage; Frequency: Monthly; Goal: Achieve 80%. 3️⃣ Mean Time To Resolution (MTTR): Time taken to fix a bug. Unit: Hours; Frequency: Monthly; Goal: Reduce by 30%. OKRs: 1️⃣ Objective: Improve software quality. Result: Reduce production bugs by 50% in Q4. 2️⃣ Objective: Accelerate delivery. Result: Release new features 20% faster by Q2. 3️⃣ Objective: Enhance user experience. Result: Achieve a 90% user satisfaction rating by year-end. #

**Title: How to Implement Continuous Testing in Agile** In the fast-paced world of Agile development, Continuous Testing has emerged as a crucial practice to ensure quality and speed go hand in hand. Traditional testing methods can no longer keep up with Agile's iterative cycles, as they often become bottlenecks, delaying releases and hindering innovation. Continuous Testing, however, seamlessly integrates testing into every stage of the development pipeline, allowing teams to identify and rectify issues almost instantaneously. By embedding testing throughout the development process, teams can reduce defects, improve user satisfaction, and enhance overall productivity. Success stories from leading tech companies underscore how Continuous Testing has helped them maintain high standards while shipping features faster than ever. In this guide, we’ll break down the step-by-step implementation of Continuous Testing in Agile, supported by data-driven results. **Step-by-Step Guide to Implement Continuous Testing in Agile:** 1. **Define Clear Objectives**: - Understand what success looks like for your team. - Establish key performance indicators (KPIs) to measure progress. 2. **Shift Left in Testing**: - Involve testers early in the development lifecycle. - Prioritize early identification of potential issues. 3. **Automate Repetitive Tasks**: - Use automation frameworks (e.g., Selenium, Jenkins) to streamline testing workflows. - Focus on automated regression testing to catch issues faster. 4. **Continuous Integration (CI) Tools**: - Integrate testing frameworks with CI tools like Travis CI or GitLab CI/CD. - Enable frequent and consistent testing cycles. 5. **Feedback Loops**: - Implement agile feedback loops to ensure test results are communicated promptly. - Foster collaboration between developers, testers, and product owners. 6. **Prioritize Testing Environments**: - Set up reliable staging environments that mimic production. - Use containerization (e.g., Docker) to manage environments consistently. 7. **Test at Multiple Levels**: - Conduct unit tests, integration tests, and system tests. - Ensure comprehensive test coverage across different aspects of the software. **Success Stories and Data-Driven Results:** - **Company X** improved deployment frequency by 30% by integrating automated regression testing. - **Startup Y** reduced critical defects in production by 40% with early-stage testing. - **Enterprise Z** achieved a 50% faster time-to-market through CI/CD adoption and continuous

Agile Testing Methodologies. Agile Software Testing is a type of software testing that follows the principles of agile software development to test the software application. Agile testing is an iterative and incremental method, and the necessities, which develop during the cooperation between the customer and self-establish teams. Agile testing is an informal process that is specified as a dynamic type of testing. It is performed regularly throughout every iteration of the Software Development Lifecycle (SDLC). Customer satisfaction is the primary concern for agile test engineers at some stage in the agile testing process. This approach helps detect and fix issues early, aligning with Agile iterative development cycle. Agile Testing Methodologies are as follows: -Test-Driven Development (TDD): TDD is the software development process relying on creating unit test cases before developing the actual code of the software. It is an iterative approach that combines 3 operations, programming, creation of unit tests, and refactoring. -Behavior Driven Development (BDD): BDD is agile software testing that aims to document and develop the application around the user behavior a user expects to experience when interacting with the application. It encourages collaboration among the developer, quality experts, and customer representatives. -Exploratory Testing: In exploratory testing, the tester has the freedom to explore the code and create effective and efficient software. It helps to discover the unknown risks and explore each aspect of the software functionality.  -Acceptance Test-Driven Development (ATDD): ATDD is a collaborative process where customer representatives, developers, and testers come together to discuss the requirements, and potential pitfalls and thus reduce the chance of errors before coding begins.  -Extreme Programming (XP): Extreme programming is a customer-oriented methodology that helps to deliver a good quality product that meets customer expectations and requirements. -Session-Based Testing: It is a structured and time-based approach that involves the progress of exploratory testing in multiple sessions. This involves uninterrupted testing sessions that are time-boxed with a duration varying from 45 to 90 minutes. During the session, the tester creates a document called a charter document that includes various information about their testing. -Dynamic Software Development Method (DSDM): DSDM is an agile project delivery framework that provides a framework for building and maintaining systems. It can be used by users, developers, and testers. -Crystal Methodologies: This methodology focuses on people and their interactions when working on the project instead of processes and tools. The suitability of the crystal method depends on three dimensions, team size, criticality, and priority of the project.

"Breaking Down Agile Testing: Why It Matters for Every QA Professional" 🚀Agile Testing is a modern approach to software testing, designed to align seamlessly with the principles of Agile software development. Unlike traditional testing methods, Agile Testing happens continuously throughout the development process. This ensures that bugs are caught early, and the software evolves based on real-time feedback. Here's a breakdown in simple terms: 🔑 Key Principles of Agile Testing:🔑 1️⃣. Continuous Testing: Testing happens alongside development, not after the product is built. This saves time and effort in the long run. 2️⃣. Collaboration is Key: Testers, developers, and business teams work together closely to understand requirements and deliver quality. 3️⃣. Fast Feedback: The focus is on getting quick feedback from testing so that developers can fix issues immediately. 4️⃣. Customer-Centric: The testing is guided by what the customer needs, ensuring the final product is user-friendly. 5️⃣. Adaptability: Agile Testing embraces changes in requirements and adapts the tests accordingly. ✴️Why Agile Testing Matters:✴️ ➡️ It ensures high-quality software is delivered in short timeframes (sprints). ➡️ Reduces the risk of big bugs being discovered late in the process. ➡️ Promotes collaboration and eliminates the ‘us vs. them’ mindset between testers and developers. 🔷Example: 👉Imagine building a mobile app in 2-week cycles (sprints). After each sprint, the app is tested for functionality, performance, and user experience. If any issue arises, the development team fixes it immediately in the next cycle. This constant testing and improvement continue until the app meets the desired standards. 🔑Key Agile Testing Practices:🔑 ➡️ Test-Driven Development (TDD): 🔸Writing tests before the code is developed. ➡️ Behavior-Driven Development (BDD): 🔸Focusing on testing user behavior and expectations. ➡️ Exploratory Testing: 🔸Actively testing the app without predefined scripts to uncover hidden bugs. 💫Agile Testing is not just about finding defects; 👉 it's about building better software through teamwork, adaptability, and continuous improvement. Whether you're a developer, tester, or stakeholder, Agile Testing fosters a collaborative environment that delivers results faster and smarter. ----- Thank You for Engaging! Your time and interest mean a lot. I hope this post added value to your professional journey. Remember, knowledge grows when shared, and your feedback or thoughts can spark new ideas. Feel free to connect, share, or ask questions—let's grow together! Stay tuned for more insights, and thank you for being a part of this journey. Keep learning, keep growing! #Agiltesting #Qualityassurance #QA #Manualtesting #automationtesting #Agil

In the evolving landscape of Agile software development, the integration of manual testing, automation, and AI is essential for delivering high-quality products. Here's an in-depth exploration of this dynamic: 1. The Role of Manual Testing in Agile: Exploratory Testing: Manual testing enables testers to interact with applications without predefined scripts, uncovering unexpected issues and gaining insights into user experience. Usability and Accessibility Assessment: Human testers can evaluate the intuitiveness and accessibility of applications, ensuring they meet diverse user needs. 2. Advantages of Automation and AI in Testing: Efficiency and Speed: Automation accelerates repetitive tasks, providing immediate feedback and enabling swift adjustments, which is essential in Agile's iterative cycles. Enhanced Test Coverage: Automated tests can handle large volumes of data and execute complex scenarios, increasing overall test coverage. Predictive Analysis: AI can analyze patterns to predict potential defects, allowing proactive issue resolution. 3. Integration of Manual and Automated Testing: Hybrid Approach: Combining manual and automated testing leverages the strengths of both, ensuring comprehensive coverage and quality. Strategic Allocation: Manual testing is best for exploratory and usability tests, while automation suits regression and load testing. 4. Industry Trends and Statistics: Adoption Rates: Approximately 24% of companies have automated 50% or more of their test cases, with 33% aiming to automate between 50% to 75%. Balanced Strategies: 33% of teams target automating 50-75% of their test cases, highlighting the importance of balancing automation with manual testing. 5. Challenges and Considerations: Human Intuition: Certain aspects, like user experience evaluation, still require human judgment, which AI and automation cannot fully replicate. Resource Investment: Automation requires initial investment in tools and training, while manual testing demands skilled human resources. 6. Future Outlook: AI Integration: AI is enhancing testing by automating result analysis and bug reporting, reducing manual effort and improving efficiency. Evolving Roles: Testers are adapting to emerging technologies, with AI making testing faster, clearer, and more cost-effective. In conclusion, while automation and AI significantly enhance testing efficiency and coverage in Agile methodologies, manual testing remains indispensable for areas requiring human intuition and exploratory analysis. A balanced, hybrid approach that integrates both manual and automated testing is essential for delivering high-quality software products.

Agile Software Testing. Agile Software Testing is a type of software testing that follows the principles of agile software development to test the software application. Agile testing is an iterative and incremental method, and the necessities, which develop during the cooperation between the customer and self-establish teams. Agile testing is an informal process that is specified as a dynamic type of testing. It is performed regularly throughout every iteration of the Software Development Lifecycle (SDLC). Customer satisfaction is the primary concern for agile test engineers at some stage in the agile testing process. In Agile, testing is an ongoing activity rather than a distinct phase, focusing on collaboration between testers, developers, and stakeholders. This approach helps detect and fix issues early, aligning with Agile iterative development cycle. Following are the principles of Agile Software Testing: 1. Constant Response The implementation of Agile testing delivers a response or feedback on an ongoing basis. Therefore, our product can meet the business needs. In other words, we can say that the Product and business requirements are understood throughout the constant response. 2. Less Documentation The execution of agile testing requires less documentation as the Agile teams or all the test engineers use a reusable specification or a checklist. And the team emphases the test rather than the secondary information. 3. Continuous Testing The agile test engineers execute the testing endlessly as this is the only technique to make sure that the constant improvement of the product. 4. Customer Satisfaction In any project delivery, customer satisfaction is important as the customers are exposed to their product throughout the development process. As the development phase progresses, the customer can easily modify and update requirements. And the tests can also be changed as per the updated requirements. 5. Easy and clean code When the bugs or defects occurred by the agile team or the testing team are fixed in a similar iteration, which leads us to get the easy and clean code. 6. Involvement of the entire team As we know that, the testing team is the only team who is responsible for a testing process in the SDLC But on the other hand, in agile testing, the business analyst and the developers can also test the application or the software. 7. Test-Driven While doing the agile testing, we need to execute the testing process during the implementation that helps us to decrease the development time. However, the testing is implemented after implementation or when the software is developed in the traditional process. 8. Quick response In each iteration of agile testing, the business team is involved. Therefore, we can get continuous feedback that helps us to reduces the time of feedback response on development work.

Today' hot take on AI assisted agile software development: The more I follow the AI assisted development craze, the more I see people using LLMs to generate tests from existing code, aka test after testing. Some of the IDE plugins I've been testing out even have a quick action to generate tests. Another big movement I am seeing, mostly with the YouTube crowd, is generating whole applications from scratch using a single prompt. Yes I know it is YouTube and that system is optimized for the "wow factor" but how many developers will see that and try to use some of that "magic" in their production codebases? One big question pops into my head, why are people not using AI assistants to automate the TDD flow? Test driven development is believed to produce higher quality software (anyone have a reference to prove this?) while also creating a test suite that gives higher assurances that it will fail if some behavior changes down the road. These high assurances happen because during the TDD process, we only write enough code to make the test pass, reducing the amount of behavior that may be exercised by running the tests but not actually asserted to be correct in the test. Test after testing does not have these same benefits because you haven't seen the test go red before writing just enough code to make it go green. Anyone made some changes in a codebase and not seen the tests fail immediately because the behavior of the system has changed? I have and it makes me very nervous to make any more changes for fear of introducing new bugs. Yes I can hear those of you in the back saying "but we've successfully used LLMs to increase our test coverage, isn't that better than nothing?". Let's be real here, increasing test coverage is not the same as writing robust tests that actually assert on the behavior of your application. Sure you code is getting exercised but using a statically typed language with a strong type system will get you that assurance for free. Other folks will say that they trust the tests generated by the AI assistant. I'd bet a high percentage of those tests are correct but how much are you willing to bet they give you enough confidence to refactor you codebase freely without introducing a regression? That answer will vary by team but making the assumption that they meet your standard may be setting you up for failure down the road. What would a world look like if we partnered with an AI assistant to reduce the cost of doing TDD by asking the AI assistant to help us write the tests first? We could then use those tests to ask the AI assistant to implement just enough code to make them green knowing that if it doesn't meet our specifications then the tests will let us know. Would this whole process move the needle on TDD adoption so that more teams could get the benefits without increased costs? What do you think?

Agile and AI are transforming software testing. This new term hit me hard: ‘Agile AI.’ It means combining Agile methods with AI tools. And it’s supercharging your software testing process. (making it faster and more accurate) The industry is digging into: - Accurate Agile planning and estimation. - Improved requirement gathering. - Data-driven project insights. - AI-powered automation. - Continuous development and iteration. Here are the 5 ways they do it: 1. Accurate Agile Planning and Estimation ↳ Agile helps in breaking down tasks into manageable pieces. ↳ AI can predict timelines with better accuracy. 1. Improved Requirement Gathering ↳ Agile focuses on continuous feedback. ↳ AI can analyze user data to refine requirements. 1. Data-Driven Project Insights ↳ Agile uses regular check-ins to assess progress. ↳ AI can provide deeper insights from vast data. 1. AI-Powered Automation ↳ Agile promotes automated testing. ↳ AI can enhance test coverage and efficiency. 1. Continuous Development and Iteration ↳ Agile supports ongoing development. ↳ AI helps in identifying areas for improvement swiftly. Overcoming Agile challenges with AI support: ↳ AI can handle repetitive tasks, freeing up human testers. ↳ It can also help in identifying and fixing bugs faster. The future of software testing is Agile AI powered. As I mentioned data-driven insights, for fun. If you are starting now with Testing Automation, here are 3 tools you have to master: - Playwright - Bruno (usebruno) - Jira Using the right tools can make a big difference! ♻️ Repost if you agree