Software Testing Basics

Demystifying the Software Testing 1️⃣ 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀: Unit Testing: Isolating individual code units to ensure they work as expected. Think of it as testing each brick before building a wall. Integration Testing: Verifying how different modules work together. Imagine testing how the bricks fit into the wall. System Testing: Putting it all together, ensuring the entire system functions as designed. Now, test the whole building for stability and functionality. Acceptance Testing: The final hurdle! Here, users or stakeholders confirm the software meets their needs. Think of it as the grand opening ceremony for your building. 2️⃣ 𝗡𝗼𝗻-𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀: ️ Performance Testing: Assessing speed, responsiveness, and scalability under different loads. Imagine testing how many people your building can safely accommodate. Security Testing: Identifying and mitigating vulnerabilities to protect against cyberattacks. Think of it as installing security systems and testing their effectiveness. Usability Testing: Evaluating how easy and intuitive the software is to use. Imagine testing how user-friendly your building is for navigation and accessibility. 3️⃣ 𝗢𝘁𝗵𝗲𝗿 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗔𝘃𝗲𝗻𝘂𝗲𝘀: 𝗧𝗵𝗲 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗖𝗿𝗲𝘄: Regression Testing: Ensuring new changes haven't broken existing functionality. Imagine checking your building for cracks after renovations. Smoke Testing: A quick sanity check to ensure basic functionality before further testing. Think of turning on the lights and checking for basic systems functionality before a deeper inspection. Exploratory Testing: Unstructured, creative testing to uncover unexpected issues. Imagine a detective searching for hidden clues in your building. Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

22 Test Automation Framework Practices That Separate Good SDETs from Great Ones Here's what actually works: 1. KISS Principle Break complex tests into smaller modules. Avoid singletons that kill parallel execution. Example: Simple initBrowser() method instead of static WebDriver instances. 2. Modular Approach Separate test data, utilities, page objects, and execution logic. Example: LoginPage class handles only login elements and actions. 3. Setup Data via API/DB Never use UI for test preconditions. It's slow and flaky. Example: RestAssured POST to create test users before running tests. 4. Ditch Excel for Test Data Use JSON, XML, or CSV. They're faster, easier to version control, and actually work. Example: Jackson ObjectMapper to read JSON into POJOs. 5. Design Patterns Factory: Create driver instances based on browser type Strategy: Switch between different browser setups Builder: Construct complex test objects step by step 6. Static Code Analysis SonarLint catches unused variables and potential bugs while you code. 7. Data-Driven Testing Run same test with multiple data sets using TestNG DataProvider. Example: One login test, 10 different user credentials. 8. Exception Handling + Logging Log failures properly. Future you will thank present you. Example: Logger.severe() with meaningful error messages. 9. Automate the Right Tests Focus on repetitive, critical tests. Each test must be independent. 10. Wait Utilities WebDriverWait with explicit conditions. Never Thread.sleep(). Example: wait.until(ExpectedConditions.visibilityOfElementLocated()) 11. POJOs for API Type-safe response handling using Gson or Jackson. Example: Convert JSON response directly to User object. 12. DRY Principle Centralize common locators and setup/teardown in BaseTest class. 13. Independent Tests Each test sets up and cleans up its own data. Enables parallel execution. 14. Config Files URLs, credentials, environment settings—all in external properties files. Example: ConfigReader class to load properties. 15. SOLID Principles Single responsibility per class. Test logic separate from data and helpers. 16. Custom Reporting ExtentReports with screenshots, logs, and environment details. 17. Cucumber Reality Check If you're not doing full BDD, skip Cucumber. It adds complexity without value. 18. Right Tool Selection Choose based on project needs, not trends. Evaluate maintenance cost. 19. Atomic Tests One test = one feature. Fast, reliable, easy to maintain. 20. Test Pyramid Many unit tests (fast) → Some API tests → Few UI tests (slow). 21. Clean Test Data Create in @BeforeMethod, delete in @AfterMethod. Zero data pollution. 22. Data-Driven API Tests Dynamic assertions, realistic data, POJO response validation. Which practice transformed your framework the most? -x-x- Most asked SDET Q&A for 2025 with SDET Coding Interview Prep (LeetCode): https://lnkd.in/gFvrJVyU #japneetsachdeva

When I was a fresh on-call DevOps 10 years ago, my response plan looked like this: ➤ Panic ➤ Google ➤ Copy a StackOverflow thread from 2016 ➤ Pray it doesn't break more stuff And then came the real world of EKS clusters with 400+ microservices, ghosted pods, CostExplodingJobs™, and 3AM alerts that couldn’t wait. That’s when I built my own brown bag of 20 dead-simple one-liner commands. 1. Show pods across all namespaces → kubectl get pods --all-namespaces 2. See all events sorted by timestamp → kubectl get events --sort-by=.metadata.creationTimestamp 3. Get pods with extra details (incl. node, IP) → kubectl get pods -o wide 4. Show full pod status (best for debugging) → kubectl describe pod <pod-name> 5. Quickly check what’s crashing on this node → kubectl get pods --all-namespaces -o wide | grep <node-name> 6. Top pods by CPU usage → kubectl top pod --sort-by=cpu 7. Top pods by memory usage → kubectl top pod --sort-by=memory 8. Nodes running hot on CPU → kubectl top node --sort-by=cpu 9. See which namespaces burn the most → kubectl top pod --all-namespaces | awk '{print $1}' | sort | uniq -c | sort -nr 10. Check for services with empty endpoints → kubectl get endpoints | grep -w '\[\]' 11. List all services with their cluster IPs and ports → kubectl get svc -A 12. Trace service → pod routing → kubectl describe svc <svc-name> 13. DNS resolution from inside the cluster → kubectl run -i --tty busybox --image=busybox --restart=Never -- sh then: nslookup <svc-name> 14. Rollout history of a deployment → kubectl rollout history deployment/<deployment-name> 15. Undo last rollout (fast rollback) → kubectl rollout undo deployment/<deployment-name> 16. Restart a deployment (graceful) → kubectl rollout restart deployment/<deployment-name> 17. Tail logs from a pod → kubectl logs -f <pod-name> 18. Tail logs from a specific container → kubectl logs -f <pod-name> -c <container-name> 19. Exec into a pod (to troubleshoot live) → kubectl exec -it <pod-name> -- /bin/sh 20. Delete all completed/crashed pods → kubectl delete pod --field-selector=status.phase==Succeeded kubectl delete pod --field-selector=status.phase==Failed Which one-liner saved your life during an on-call? I’m collecting real stories for the next post. Drop yours below.

I've seen more projects derailed by a few fuzzy words than by any complex technical challenge. When it comes to a software specification, ambiguity is the enemy, and it can creep in through a few common pitfalls that destroy projects. After years in the trenches, you see the same mistakes happen. Here are three of the most common ones to avoid: ❌ Vague Words: You can't test "fast" or "easy to use". These terms are subjective. A requirement must be quantifiable. Instead of saying the system should be fast, define the acceptable response time in milliseconds. ❌ "Frankenstein" Requirements: This happens when multiple distinct actions are lumped into a single instruction , like "The system shall login and display dashboard and send notifications". If one part fails, does the whole requirement fail? Keep each action separate and testable. ❌ Designing in Disguise: Requirements should state what the system needs to do, not how the developers should build it. Specifying "use a red button with Arial font" is a design choice that ties the team's hands. Avoiding these traps is one of the most important steps in ensuring a smooth project. We illustrate these common mistakes in our new visual guide. Which of these pitfalls have you seen cause the most problems in a project? #SoftwareEngineering #RiskManagement #DigitalTransformation

My team once skipped regression testing. We thought, “The change is small. What could go wrong?” It turns out that the checkout screen crashed in production. And yes, it hit 40K live users. We had to roll back fast. The team learned a big lesson that day. Since then, I’ve paid close attention to how regression testing is done. Here are 7 common mistakes I see around regression testing 1. Testing everything every time That’s like checking every room in your house when only the kitchen light is broken. Analyze and prioritize what needs to be tested, and execute tests based on changes that are going into production. 2. Old test cases, never updated They pass. However, the features they test no longer exist, and there may be test cases that cover the same feature multiple times. Spend time maintaining and optimizing your regression test after every release. 3. Automating everything blindly Not every test needs to be automated. Some break more often than they help. Identify the appropriate set of test cases for automation, including end-to-end workflows and third-party integrations. 4. Not connected to CI/CD If your regression suite is not part of the release flow, bugs can inadvertently be introduced into production. Ensure that they can run unattended whenever you need to test. 5. No trend tracking Are you catching the same bugs again and again? That’s a pattern worth noticing. Conduct Root Cause and Trend analysis for every production release. 6. Skipping non-functional testing Just because it works doesn’t mean it’s usable or fast. Ensure you run non-functional tests related to performance, security, and other key areas for releases. 7. “Nothing changed, so no testing.” Even untouched modules can break, especially when they're integrated with other modules or applications. It is not the shiny new feature that breaks trust. It’s when the thing that used to work suddenly does not. A static regression suite is like locking your doors but leaving the windows open. Your product changes. So should your tests. Regression isn’t a fixed asset. It should evolve in tandem with your product, your users, and the way your team operates. What’s one mistake you have made in regression testing? Please share your experience👇 #SoftwareTesting #RegressionTesting #QualityAsssurance #TestMetry 

Don’t Focus Too Much On Writing More Tests Too Soon 📌 Prioritize Quality over Quantity - Make sure the tests you have (and this can even be just a single test) are useful, well-written and trustworthy. Make them part of your build pipeline. Make sure you know who needs to act when the test(s) should fail. Make sure you know who should write the next test. 📌 Test Coverage Analysis: Regularly assess the coverage of your tests to ensure they adequately exercise all parts of the codebase. Tools like code coverage analysis can help identify areas where additional testing is needed. 📌 Code Reviews for Tests: Just like code changes, tests should undergo thorough code reviews to ensure their quality and effectiveness. This helps catch any issues or oversights in the testing logic before they are integrated into the codebase. 📌 Parameterized and Data-Driven Tests: Incorporate parameterized and data-driven testing techniques to increase the versatility and comprehensiveness of your tests. This allows you to test a wider range of scenarios with minimal additional effort. 📌 Test Stability Monitoring: Monitor the stability of your tests over time to detect any flakiness or reliability issues. Continuous monitoring can help identify and address any recurring problems, ensuring the ongoing trustworthiness of your test suite. 📌 Test Environment Isolation: Ensure that tests are run in isolated environments to minimize interference from external factors. This helps maintain consistency and reliability in test results, regardless of changes in the development or deployment environment. 📌 Test Result Reporting: Implement robust reporting mechanisms for test results, including detailed logs and notifications. This enables quick identification and resolution of any failures, improving the responsiveness and reliability of the testing process. 📌 Regression Testing: Integrate regression testing into your workflow to detect unintended side effects of code changes. Automated regression tests help ensure that existing functionality remains intact as the codebase evolves, enhancing overall trust in the system. 📌 Periodic Review and Refinement: Regularly review and refine your testing strategy based on feedback and lessons learned from previous testing cycles. This iterative approach helps continually improve the effectiveness and trustworthiness of your testing process.

I used to do traditional "unit tests", where each class is tested individually and all of its dependencies are mocked. But over time, I found it to be a really bad practice. Here's why. When you are mocking dependencies, you are making assumptions about them. And there is always a chance that your assumptions are wrong. This is especially true for complex systems. The more dependencies you have - the greater the chance of making the wrong assumption. This leads to a problem. You may have 100% code coverage, but your tests are useless. Once you start running your system end-to-end, you find that it doesn't work the way you assumed. Now, there's a lot of rework. You have to fix a bunch of integration defects. And fixing integration defects sometimes requires more effort than writing the thing in the first place! What I prefer to do instead is having tests at the entry point of an entire module (library, executable, etc.) and use real dependencies. I keep mocking to the minimum. This way, all inner classes are still being tested implicitly. I still get code coverage close to 100%. The tests are still fast. But now, there's little rework (if any) because I no longer have to make assumptions about the system. There's a much smaller chance of getting it wrong. A system developed under these tests still works as expected when launched end-to-end.

Data engineers, have you ever written test cases that only cover specific, hardcoded inputs? You might feel confident that your code works, but what happens when it encounters an edge case you didn’t anticipate? Traditional testing can leave gaps, especially when dealing with dynamic data like API responses or user inputs. Imagine having tests that automatically cover a wide range of scenarios, including those tricky edge cases. With property-based testing, you can generate diverse test cases that push your code to its limits, ensuring it performs reliably under various conditions. This approach can dramatically increase the robustness of your code, giving you more confidence in its correctness. Enter the `hypothesis` library in Python. Instead of manually writing test cases for every possible input, `hypothesis` generates a wide range of inputs for you, systematically exploring your code’s behavior. 1. Traditional Test Case (left side): Here’s a typical `pytest` test for a `transform` function that adds a URL to a list of exchanges: This works for specific inputs, but what about other cases? What if the list is empty, or the exchange names are unusually long? A single test case won’t cover all possibilities. 2. Property-Based Testing with `hypothesis` (right side): With `hypothesis`, we can generate varied inputs to ensure the `transform` function handles them correctly. The Benefits: 1. Comprehensive Coverage: This approach ensures your code is tested against a wide range of inputs, catching edge cases you might miss with traditional tests. 2. Increased Confidence: You can trust that your code is robust and ready for production, no matter what data it encounters. 3. Efficiency: Property-based tests can replace dozens of manual test cases, saving time while increasing coverage. Property-based testing with `hypothesis` is a game-changer for data engineers. By automating the creation of diverse test cases, you ensure your code is reliable, robust, and production-ready. #dataengineering #python #propertybasedtesting #hypothesis #unittesting #techtips

Only one test matters: "When we deliver this change, it should provide the expected value." That test can only be run in production. All the additional testing before production is only there to give us confidence that we are not breaking anything. - We've not created a security problem. - We've not degraded performance - The UX doesn't suck - We haven't broken existing behaviors - etc. We can never prove we've found every problem; we can only be relatively confident. The longer it takes us to gain that confidence, the more money it will cost, and the larger the batch will be. Larger batches make it harder to find problems and enable us to deliver more of the wrong thing. Even if we've not introduced a new problem and we build exactly the right thing, the delays and added costs still lower the value of the delivered batch. Measure your quality process from idea to delivery and reduce the cost and size of every delivery. This will reduce the number of failures of the only test that matters. Want tips? Check out Flow Engineering by Steve Pereira and Andrew Davis

𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐢𝐬 𝐦𝐚𝐭𝐮𝐫𝐢𝐧𝐠. 𝐁𝐮𝐭 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐛𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤? 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐠𝐡𝐨𝐬𝐭𝐬. We’ve seen toolkits. We’ve seen use cases. What we haven’t seen - until now - is a way to understand how agents behave once they’re deployed and left to operate on their own. Because here’s the problem: → LLM-based agents are inherently stochastic → Same input, different outputs, unpredictable tool invocations → “Works in demo” doesn’t scale to production The authors propose a solution: Treat every agent trajectory - tool calls, decisions, delegation patterns - as a process log. Then apply process mining and causal discovery to see what’s consistent, and what’s not. Why this matters: Most failures in multi-agent setups aren’t logic bugs. They’re mismatches between what the developer intended and what the agent improvised. → You thought only the Calculator could call math tools → But the Manager quietly started using them too → Why? The prompt was too vague. The role permissions too soft. Using causal models, LLM-based static analysis, and trajectory logging, this approach reveals: → “Breaches of responsibility” between agents → Hidden variability in execution flows → Ambiguity in natural language prompts that leads to divergence → Unstable behavior even with temperature = 0 This isn't just academic. It's the early foundation for something we don’t yet have: DevOps for agentic systems. Implications for enterprise AI teams: → You need observability pipelines for your AI agents, not just dashboards for humans → Prompt engineering is not enough - you need static validation and runtime tracing → Failure analysis must shift from error messages to behavioral forensics Just like we had to build test harnesses, CI/CD, and tracing for microservices, we’ll now need: → Agent trajectory logs → Causal maps of tool flows → Static analysis of prompt intent vs observed actions Because in agentic systems, debugging isn't about fixing code. It’s about understanding emergent behavior. Would love to hear from: → Builders working with CrewAI, LangGraph, AutoGen → Teams deploying autonomous workflows in production → Researchers thinking about agent alignment and runtime guarantees What would your agent observability stack look like? And who owns the problem when the AI decides to go off-script?