Building Test Suites for Software Quality

When we have a story that involves multiple steps, should our testing look at all the steps in the story together, or isolate them one at a time? What if we are scripting a check of these steps? Do you write one script to cover the whole sequence, or write a script per step to check each one individually? Or do you do both? The problem presents itself especially in automation where behaviors tend to be rigid and fixed. If a script covers all the steps in a story, then a single failure along the way prevents getting to the final target check at the end. If a script covers each step on its own, assuming that is possible, then bugs that arise from transition through the whole are missed. We may offer to cover it all, writing the union of every possible step on its own or combined into every sequence, partial and complete, but the explosion of cost and time on running and maintaining a monstrosity of code will likely overwhelm us. There is no magic formula. You have to make choices. You probably want to complement multi-step sequences with isolated steps. You probably want to create shortcut paths to important functionality so getting there is not impeded by steps along the way, but you want individual coverage for those steps as well. Automated suites tend to be more consistent in behavior the more isolated their coverage, but they tend to find fewer bugs that way as well. A good idea is to ask yourself what you are testing for and why. If you are writing automated suites, when will they be run, and for what reason? Solve for those purposes in mind. If you are looking for hard to catch bugs, you might favor longer sequences that cover more ground. If you are trying to quickly spot regressions, you might favor isolation. Other differences in purpose may guide different strategies. It is a judgment call that takes practice. #softwaretesting #softwaredevelopment #ialreadydidanaprilfirstjokepostsothisoneisreal

If your test suite is 95% unit tests... you might be testing the wrong thing. Unit tests are great: - fast - cheap - perfect for domain rules But most real bugs don’t live in pure functions. They show up when code meets reality: - database mappings - transactions - serialization - config - external services That’s why my rule of thumb is: - 𝗨𝗻𝗶𝘁 𝘁𝗲𝘀𝘁 𝘆𝗼𝘂𝗿 𝗱𝗼𝗺𝗮𝗶𝗻 𝗹𝗼𝗴𝗶𝗰 - 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝘀𝘁 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 Best setup I’ve used: integration tests with Testcontainers. If it runs in Docker, you can spin up real dependencies locally and in CI without pain. Want to set it up from scratch? Here's a complete from-scratch guide: https://lnkd.in/dAfw5dtM What’s your testing setup? --- Do you want to simplify your development process? Grab my Clean Architecture template here and save 7 days of development time: https://lnkd.in/dYNsNb52

Most automation engineers obsess over UI automation. But the truth is — UI is just the tip of the testing iceberg. Let’s break this down. There are multiple layers where tests should live: Unit Tests → Fast and precise. → Catch issues early. → Tools/Framework/Libraries: JUnit, NUnit, Pytest, Mocha Component/Module Tests → Validate individual pieces in isolation. → Especially useful in frontend frameworks. → Tools/Framework/Libraries: React Testing Library, Vue Test Utils API Tests → Validate business logic and service contracts. → Great for catching bugs before they reach the UI. → Tools/Framework/Libraries: Postman, Rest Assured, Jest, Pytest + Requests Integration Tests → Ensure all systems talk to each other correctly. → Covers database, third-party APIs, and internal services. → Tools/Framework/Libraries: Pytest, TestContainers, WireMock Database Tests → Validate migrations, data constraints, and stored procedures. → Tools/Framework/Libraries: DBUnit, Flyway, SQLTest UI Tests → Useful, but often slow and flaky. → Should be minimal and well-targeted. → Tools: Playwright, Cypress, Selenium, Appium (for mobile) If your entire test suite lives only at the UI layer, you’re doing your team a disservice. Test smarter — not just at the top. I’ve explained how to structure and design your tests across these layers in my book Ultimate Test Design Patterns for Layered Testing. This isn't just theory — it's a blueprint for building robust, maintainable, and scalable automation. Want to know which test belongs where? Start by understanding the layers first. #TestAutomation #SDET #QualityEngineering #TestingStrategy #SoftwareTesting #TechLeadership

Let's be real: If your testing strategy is all about Playwright and Cypress, you're missing the bigger picture. Here's the truth about REAL test automation: Backend is where the magic happens. While everyone's obsessing over button clicks and page loads, the real champions are testing: - API contracts and edge cases that break production - gRPC services that power your critical operations - Database consistency across SQL/NoSQL systems - Message queues handling massive data flows - Microservice communication under stress That pretty UI automation? It's just the cherry on top. The real work is in the foundation. This is how it's done: 1. Lock down your API contracts first 2. Nail your database interactions 3. Master your message queue flows 4. Verify your service communications 5. THEN worry about that UI layer Remember: When production goes down, it's rarely because a button wasn't clickable. It's because someone didn't test their database transactions, message queues, or API edge cases. Been doing this for years, and I'll say it loud: Strong backend testing is non-negotiable. Your users don't care about your 1000 UI tests if their data is corrupted or their transactions are failing. Stop playing in the shallow end. Dive deep. That's where the real quality lives. #TestAutomation #QualityEngineering #SDET #BackendTesting #SoftwareQuality

500+ tests running daily. Zero manual trigger. Confidence up. Stress down. When I joined PLANOLY, the product complexity was growing fast. But the automation coverage wasn’t keeping up. Releases started to feel risky and took long. I built a scalable Cypress framework from scratch. It followed the Page Object Model and supported data-driven testing. It was designed to be maintainable for the long haul. Next, I integrated it with GitHub Actions. Full test suites now run automatically on nightly builds so I can see the result first thing in the morning. The result? Over 500 automated tests running multiple times a week. They provide fast feedback, catch regressions early, and boost confidence across the team. If you’re working on scaling test automation, I’m happy to share lessons from this experience. What’s the biggest challenge you’ve faced with automation at scale?

We all know about tech debt. But what about test debt? It builds up when your QA process can’t keep pace with your product. And most teams have more of it than they realize. Eventually, test coverage drops. Confidence in releases takes a hit. And teams start playing it safe... or worse, skipping testing altogether just to ship faster. At that point, fixing it feels overwhelming. Because the tools you used to build the test suite are now the bottleneck. Spot test debt before it wrecks your roadmap: - UI tests that constantly break with minor UI changes - Manual test cases that never got automated - Unoptimized automated tests that take hours to run - Legacy scripts nobody wants to maintain - And no visibility into what’s actually covered And how to manage it? 👉 Audit your current test suite (flag tests that fail often or add no value) 👉 Automate smarter (prioritize high-value, repeatable flows) 👉 Simplify test ownership (avoid tools that require heavy dev time) 👉 Track coverage, not just quantity (make sure what matters is tested) 👉 Lastly, counter it with intelligent test automation (maintainable, resilient, and human-readable) Read more here: https://t2m.io/R08TK9n #softwaretesting #qa #testautomation

After 25+ years in #QA, one architectural pattern keeps repeating. Most escaped defects were not caused by people being unable to test well. They were caused by the system’s inability to be re-tested frequently enough. Modern software changes constantly. Daily commits. Daily merges. Daily deployments. Humans can test deeply. But a system without automation cannot revalidate behavior every day, across environments, at scale. That is where defects escape. Automation exists for one core architectural reason: repeatability at speed. High-quality automated coverage enables a system to: 1) Re-run the same critical paths daily or continuously 2) Revalidate regression after every meaningful change 3) Preserve confidence that yesterday’s behavior still works today 4) Allocate human effort to exploration, risk analysis, and design feedback - not repetition When automation is missing, the system is forced into trade-offs: 1) Test less often 2) Test smaller slices 3) Rely on memory, heroics, and hope Hope is not a strategy. The goal of automation is not to replace humans. It is to make frequent, repeatable validation a built-in property of the system. Without that property, quality cannot keep up with change. That lesson eventually appears in every large system. #QualityEngineering #TestAutomation #QA #SoftwareTesting #QASolver

Dominoes are for games, not for tests. Strictly follow the Test Isolation Principle. If Test A fails, Test B should not care. If Test B depends on Test A’s data, you don't have a test suite. You have a house of cards. One minor UI change shouldn't trigger 50 unrelated red flags. Why isolation matters: ------------------------- ➡️ Zero Side Effects: One test’s "garbage" shouldn't become another test’s "input" ➡️ Order Independence: You should be able to run your tests in reverse, or in parallel, without a single failure. ➡️ Debugging Sanity: When a test fails in an isolated environment, you know exactly where the issue is. You don't have to spend two hours "chasing the ghost" through three previous test files. How to enforce it: -------------------- ➡️ Reset state between tests: Every test starts from a "clean slate." ➡️ Use Hooks: Leverage test.beforeEach to set up specific conditions and test.afterEach to tear them down. ➡️ Avoid Shared Global State: If you’re using a database, use transactions or unique IDs for every run to prevent data bleeding. Isolation is the key to CI/CD confidence. If your tests are flaky, your team will eventually stop trusting them. And a test suite that no one trusts is just expensive noise. Keep your tests independent. Keep your sanity intact.