Using Automated Testing in Software Development

Before you automate API tests, you need to understand the API first. APIs don’t have a UI. You can’t just click around and see what happens. That’s why most QA engineers struggle with API automation - they skip the exploration phase and jump straight to writing code. The right workflow: 1. Explore the API Use these tools to understand what the endpoints do: 📌 Browser Network Tab - See real API calls your app makes ∙ Right-click → Inspect → Network tab ∙ Watch live requests/responses ∙ Copy exact headers, payloads, status codes 📌 Swagger UI - Interactive API documentation ∙ Auto-generated from backend code ∙ Shows all available endpoints ∙ Try requests directly in the browser ∙ See example responses 📌 Postman - Manual API testing tool ∙ User-friendly interface for building requests ∙ Set headers, params, request bodies ∙ View responses in detail ∙ Save and organize API calls 2. Verify with Postman Once you understand the endpoint: ∙ Recreate the request in Postman ∙ Verify it works as expected ∙ Test different scenarios manually ∙ Document the expected behavior 3. Write automation code Now you can automate with confidence: ∙ You know what the endpoint does ∙ You know what success looks like ∙ You know what edge cases to test ∙ Your tests will be realistic and reliable The mistake most QAs make: Writing API tests without understanding the API first. Then wondering why tests are flaky or don’t catch real bugs. Bottom line: Manual exploration → Postman verification → Automation code Skip the first two steps, and your automation will be guesswork. Learn API testing + automation with Playwright in our free community 👉 https://lnkd.in/gqSnguXu #QA #TestAutomation #APITesting #Postman #Swagger #SDET #SoftwareTesting #AutomationTesting #Playwright

🛠️ What Running Test Automation Involves 🔎 📌 On-Demand Test Automation: This approach allows teams to execute test automation whenever there is a requirement to do so. It can be integrated into various stages of the development process, such as during product development, the addition of new features, or when there are new developments in testing methodologies. 📌 Timed Test Automation: Test automation can be triggered based on time. Initially, automation may take minutes due to fewer iterations, but as the number of iterations and version numbers increases, it may take hours. Running automation tests overnight is a common practice to analyze new changes to the software. 📌 Activity-Based Test Automation: As the application grows, developers shift from time-based triggers to activity-based triggers. The goal here is to target changes in the application, which can include updates, new features, or modifications to the existing features. 📌 Regression Testing: Test automation is particularly useful for regression testing, where previously implemented functionalities are tested to ensure that new changes or updates haven't introduced any unintended side effects or regressions. 📌 Parallel Execution: To speed up the testing process, automation tools often support parallel execution of test cases across multiple environments or devices. Parallel execution helps reduce the overall testing time, allowing teams to achieve faster feedback cycles and accelerate time-to-market for their products. 📌 Integration with Continuous Integration/Continuous Deployment (CI/CD): Test automation can be seamlessly integrated into CI/CD pipelines to automate the testing process as part of the overall software delivery pipeline. Automated tests can be triggered automatically whenever new code changes are committed, ensuring that each code change is thoroughly tested before deployment to production. 📌 Reporting and Analysis: Test automation tools often provide detailed reports and analytics on test execution results, including test coverage, pass/fail status, execution time, and more. These reports help stakeholders make informed decisions about the quality of the software and prioritize areas for improvement. 📌 Maintenance and Refactoring: Test automation requires ongoing maintenance and refactoring to keep test suites up to date with changes in the application codebase. As the application evolves, test scripts may need to be updated or refactored to accommodate new features or changes in functionality. 📌 Scalability and Flexibility: Test automation frameworks should be scalable and flexible to accommodate the evolving needs of the organization and the application. Scalable automation frameworks can handle large test suites efficiently, while flexible frameworks allow for easy customization and extension to support new testing requirements.

Test automation involves using specialized tools and scripts to automatically execute tests on software applications. The primary goal is to increase the efficiency and effectiveness of the testing process, reduce manual effort, and improve the accuracy of test results. ⭕ Benefits: ✅ Speed: Automated tests can run much faster than manual tests, especially when running large test suites or repeated tests across different environments. ✅Reusability: Once created, automated test scripts can be reused across multiple test cycles and projects, saving time in the long run. ✅Coverage: Automation can help achieve broader test coverage by executing more test cases in less time. It can also test various configurations and environments that might be impractical to test manually. ✅Consistency: Automated tests execute the same steps precisely each time, reducing the risk of human error and improving the reliability of the tests. ✅Regression Testing: Automated tests are particularly useful for regression testing, where previously tested functionality is checked to ensure it still works after changes are made. ⭕Challenges: ✅Initial Setup: Creating and maintaining automated tests requires a significant initial investment in terms of time and resources. ✅Maintenance: Automated tests need to be updated as the application changes. This can lead to additional maintenance overhead, especially if the application evolves frequently. ✅Complexity: Developing and managing automated tests can be complex, particularly for applications with dynamic or changing interfaces. ✅False Positives/Negatives: Automated tests might produce false positives or negatives if not carefully designed, leading to misleading results. ⭕Common Tools: ✅Selenium: A widely used tool for web application testing that supports various programming languages. ✅JUnit/TestNG: Frameworks for Java applications that provide annotations and assertions for unit testing. ✅Cypress: A modern testing framework for end-to-end testing of web applications. ✅Appium: An open-source tool for automating mobile applications on various platforms. ✅Jenkins: Often used in continuous integration/continuous deployment (CI/CD) pipelines to automate the execution of test suites. ⭕Best Practices: ✅Start Small: Begin with a few test cases to build your automation framework and gradually expand as you refine your approach. ✅Maintainability: Write clean, modular test scripts that are easy to maintain and update. ✅Data-Driven Testing: Use data-driven approaches to test various input scenarios and ensure comprehensive coverage. ✅Integrate with CI/CD: Incorporate test automation into your CI/CD pipeline to ensure automated tests run with each code change. Review and Refactor: Regularly review and refactor your test scripts to improve their efficiency and reliability. In summary, test automation can significantly enhance the testing process, but it requires thoughtful implementation and ongoing maintenance to be effective.

🔧 10 Strategies to Enhance the Reliability of Your Automation Tests Navigating the complexities of automation testing is key to maintaining high software quality. Here are some proven strategies to enhance the reliability of your automation tests: 1. Regularly Execute and Update Scripts Ensure your scripts are always up-to-date and ready for any testing demands by executing and updating them regularly. 2. Automate Comprehensive Code and Results Analysis Identify issues early by performing in-depth reviews of your code and results, helping to catch bugs before they escalate. 3. Implement Exception Handlers Incorporate exception handlers in your scripts to manage unexpected errors without halting the entire testing process. 4. Integrate Self-Healing Features Leverage self-healing tools that enable your scripts to automatically adjust to minor changes, minimizing manual modifications. 5. Employ Version Control Systems Efficiently track all script changes with version control systems, simplifying updates and enabling easy rollbacks when needed. 6. Use Continuous Integration Tools Adopt continuous integration tools to facilitate seamless automation deployments and updates, ensuring a smooth and consistent testing process. 7. Optimize Test Data Management Utilize accurate and relevant data for your tests to achieve consistent and reliable outcomes. 8. Enhance Logging and Reporting Refine your logging and reporting practices to quickly identify test failures, accelerating the troubleshooting process. 9. Ensure Cross-Browser Compatibility Conduct tests across various browsers to ensure your scripts perform optimally for a diverse audience. 10. Enable Comprehensive Defect Detection Ensure your scripts are capable of detecting defects and thoroughly validating expected results. Have you encountered similar challenges in your automation testing journey? What strategies have you found effective? Share your experiences! #TestMetry #TestAutomation #SoftwareTesting #QualityAssurance

AI-Informed Test Automation Engineers The Reality of Test Automation Today While many imagine test automation engineers spending their days writing new test scripts, the reality is quite different. Most of their time is consumed by maintaining existing test code that breaks due to website changes, and worrying about all the new untested features or backlog of tests yet to be automated. Even more concerning, traditional test automation often takes longer than a typical sprint cycle to implement. This timing gap means new features frequently ship before automated tests are ready, leaving critical functionality to be verified only through manual and infrequent testing. Traditional automation scripts, especially low-code, and no-code solutions, have significant blind spots. They typically follow hardcoded sequences — finding elements, clicking them, entering form values, and verifying specific strings or states. These scripts navigate through pages that might have serious accessibility issues, performance problems, or usability flaws, yet detect none of these issues. The AI-Informed Approach to Test Automation AI-informed test automation engineers transform this landscape in two significant ways: It takes only minutes to AI-Inform existin test automation scripts. Automation engineers need only add a simple ai_check() method to their automation scripts, called at strategic points in their test flowsto add additional test coverage. This addition enables automatic quality checks across nine different dimensions, identifying bugs that traditional automation would miss. This represents a dramatic shift on coverage and value from automated test scripts—when was the last time your test automation actually found a bug? Best Part: A light version is opensource for Python/Selenium/Playwright:. Code and instructions are @ https://lnkd.in/gYwCv-ji The XBOSoft and Checkie.AI Partnership XBOSoft and Checkie.AI have joined forces to identify effective AI integration strategies for software testing. We share our current thinking on how to create AI-Informed versions of traditional testing roles and business processes, with real-world AI Tooling and practices, and we will even some of the things that didn't work well so you don't have to learn the same mistakes 🤔 We have an upcoming free webinar on March 19th to share this vision and what we have learned: https://lnkd.in/giKcfb7C Follow/connect with me here for more details on this topic every day this week.

𝗛𝗼𝘄 𝗚𝗼𝗼𝗴𝗹𝗲 𝗨𝘀𝗲𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗧𝗲𝘀𝘁𝘀 𝗧𝗼 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗮 𝗛𝗶𝗴𝗵-𝗧𝗿𝘂𝘀𝘁 𝗖𝘂𝗹𝘁𝘂𝗿𝗲? One of the case studies discussed in the book "The DevOps Handbook" by Gene Kim et al. is that of Google, which effectively employed automated testing to achieve rapid innovation and stay ahead of its competition (Chapter 10). Here are some key takeaways from Google's approach to automated testing: 𝟭. 𝗖𝗼𝗺𝗺𝗶𝘁-𝘁𝗼-𝗗𝗲𝗽𝗹𝗼𝘆 𝗧𝗶𝗺𝗲. One of the metrics that Google monitors closely is the time it takes from when code is committed to when it's deployed. This metric captures the efficiency of the build, test, and deploy process. Automated testing plays a significant role in reducing this time by quickly catching defects. 𝟮. 𝗦𝗺𝗮𝗹𝗹, 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝘀. Google practices frequent and small code integrations. This reduces the complexity of each change, making it easier to test and verify. Automated tests ensure that each of these small integrations maintains existing functionality. 𝟯. 𝗣𝗲𝗿𝘃𝗮𝘀𝗶𝘃𝗲 𝗧𝗲𝘀𝘁 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Google has extensive automated tests at all levels - unit, integration, and system tests. Every code check-in is run against these tests, which helps ensure high quality. 𝟰. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆. The developer who writes the code is responsible for the quality of that code. If a developer checks in code that breaks the build or fails tests, it's their responsibility to fix it. This culture of ownership is enabled by comprehensive automated testing. 𝟱. 𝗛𝘂𝗴𝗲 𝗧𝗲𝘀𝘁 𝗚𝗿𝗶𝗱. Google maintains a vast test grid infrastructure to run automated tests. This allows tests to be run in parallel on thousands of machines, delivering rapid feedback to developers. 𝟲. 𝗙𝗹𝗮𝗸𝘆 𝗧𝗲𝘀𝘁 𝗤𝘂𝗮𝗿𝗮𝗻𝘁𝗶𝗻𝗲. Google recognizes that not all automated tests are perfect. Tests that fail inconsistently (often due to issues such as race conditions) are termed "flaky." Rather than removing these tests or letting them block the development pipeline, Google quarantines them. 𝟳. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽𝘀. Automated testing isn't just about catching defects; it's about providing developers with fast feedback on their changes. Google's testing infrastructure provides developers with detailed information on test failures, enabling them to diagnose and resolve issues quickly. 𝟴. 𝗛𝗶𝗴𝗵 𝗧𝗲𝘀𝘁 𝗖𝗼𝘃𝗲𝗿𝗮𝗴𝗲. Google strives for high coverage to ensure that automated tests validate most of its codebase. This isn't about reaching a certain percentage for the sake of metrics but ensuring that critical code paths are thoroughly tested. Some other impressive statistics: 🔹 𝟰𝟬,𝟬𝟬𝟬 𝗰𝗼𝗱𝗲 𝗰𝗼𝗺𝗺𝗶𝘁𝘀/𝗱𝗮𝘆.  🔹 𝟱𝟬,𝟬𝟬𝟬 𝗯𝘂𝗶𝗹𝗱𝘀/𝗱𝗮𝘆 (on weekdays, this may exceed 90,000). 🔹 𝟭𝟮𝟬,𝟬𝟬𝟬 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝘁𝗲𝘀𝘁 𝘀𝘂𝗶𝘁𝗲𝘀. 🔹 𝟳𝟱 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝘁𝗲𝘀𝘁 𝗰𝗮𝘀𝗲𝘀 𝗿𝘂𝗻 𝗱𝗮𝗶𝗹𝘆. Image: "The DevOps Handbook" authors.

QA/Automation Test Engineer Interview Questions & Answers Q1. What is the Software Testing Life Cycle (STLC) and why is it important? A: STLC consists of phases such as requirement analysis, test planning, test case design, test environment setup, test execution, defect tracking, and test closure. It is important because it ensures that testing is systematic, thorough, and integrated with the overall development process, thereby minimizing the risk of releasing defective software. Q2. How do you decide which test cases to automate? A: I prioritize automating repetitive, time-consuming, and high-risk test cases that require frequent execution (such as regression tests). Stable features that are less likely to change are ideal candidates, as are tests that can be integrated into a continuous integration/continuous delivery (CI/CD) pipeline. Q3. What tools have you used for automation testing, and how have you integrated them into CI/CD pipelines? A: I have used tools such as Selenium WebDriver for web testing, along with frameworks like TestNG or JUnit. In addition, I have experience integrating automated tests into CI/CD pipelines using Jenkins, where tests are triggered automatically upon code commits to quickly detect issues. Q4. Describe a flaky test. How do you handle it? A: A flaky test is one that produces inconsistent results—passing sometimes and failing at other times—even without code changes. To handle flaky tests, I analyze the test for timing issues, add explicit waits, stabilize the test environment, and, if necessary, isolate the test for further troubleshooting. Q5. What is the difference between functional and non-functional testing? A: Functional Testing: Validates that the system performs according to the specified functional requirements (e.g., user login works as expected). Non-Functional Testing: Evaluates aspects such as performance, security, usability, and scalability that do not relate directly to specific functions. Q6. How do you maintain and update automation test scripts as the application evolves? A: I follow best practices such as modularizing test code, using design patterns like the Page Object Model (POM), and maintaining scripts in version control systems (e.g., Git). Regular reviews and updates ensure that the automation suite adapts to changes in the application. Q7. How do you integrate testing into a CI/CD workflow? A: In a CI/CD environment, automated tests are incorporated into the build process (using tools like Jenkins) so that every code change triggers test execution. This immediate feedback loop helps catch defects early, ensures the stability of the application, and supports rapid delivery cycles.

During my time at LoopQA, I've found that an effective model for leveraging automation involves a clear division of responsibilities between development and QA teams. I'll caveat this by saying, I generally advocate for developers to refactor the automation they break, but the reality is that most organizations aren't willing to invest their time into it. Hence, having a dedicated QA team for E2E tests often proves more practical. Here's a typical and highly functional workflow: Local Development: -Developers work on their local environments. -They run unit tests and integration tests to ensure their code is functioning as expected. Dev/Test Environment: -Code is merged into a shared Dev/Test environment. -Unit/Integration Tests: These tests must pass before any merge can occur. If they fail, the merge fails. -E2E Sanity Tests: These are also run, but they can fail for both valid and invalid reasons. Developers need to be adept at identifying actual bugs versus new legitimate failures. -Automation teams step in to refactor failing E2E tests to ensure stability. A common problem here is when dev teams opt not to refactor failing tests, leading to false negatives and potential issues down the line. Stage/Pre-Prod Environment: -After passing all tests, the code moves to the Stage/Pre-Prod environment. -Any merge to this environment is contingent on passing E2E tests along with unit and integration tests. Production Environment -Finally, the tested and validated code is merged into the Production environment. Key Insights: -This model ensures that unit/integration tests act as crucial gatekeepers, providing immediate feedback to developers. -E2E tests, maintained by the QA team, ensure overall system validation and highlight integration issues early. -Collaboration and clear division of responsibilities between dev and QA teams maximize the value of automation, leading to robust and reliable code delivery. #SoftwareDevelopment #QA #Testing #DevOps #SoftwareQuality #Automation #ContinuousIntegration #ContinuousDeployment

Traditional automated testing promises efficiency, but the reality is that tests crumble at the slightest UI change. It’s an all too common scenario: Spend weeks writing the perfect test, only for a minor button update to make half your test flash red. This ensues a cycle of constant firefighting that leaves QA teams exhausted and quality taking a hit. But what if tests could evolve as your product does? 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘀𝗵𝗶𝗻𝗲𝘀. At testRigor, we’ve helped companies like Netflix and Cisco reduce their reliance on implementation details and make their tests more stable and easier to maintain. We do this by marrying AI’s adaptability with human context. How? By allowing tests to be written in plain English. This approach doesn’t just make tests more stable — it captures nuances that often slip through the cracks of traditional automation. Product managers gain direct visibility into test cases, finally bridging the gap between vision and execution. Developers receive clear, actionable feedback, pinpointing issues accurately. QA team tackles complex edge cases and lets AI handle the grunt work. The result? A virtuous cycle of faster iterations, better products, and happier customers. Make your QA process an accelerator, not a bottleneck >> https://lnkd.in/eijgpWTj #AI #Automation #softwareengineering #softwareengineering

Unit testing is a fundamental software testing approach in which individual components or functions of a program are tested independently to verify that each unit performs as expected. By isolating units from the rest of the system, developers can detect defects early in the development cycle, simplify debugging, and ensure robust code quality. Unit testing supports safe code refactoring, facilitates continuous integration, and serves as a safeguard against regressions. Leveraging tools like JUnit, GoogleTest, or PyTest, developers use automated test scripts along with mocks and stubs to simulate dependencies and validate outcomes through assertions—making unit testing a critical practice in modern software engineering.