Handling Failing Test Suites in Software Engineering

I've reviewed hundreds of test suites over the past few years, and I keep seeing the same patterns that turn promising automation into maintenance nightmares. So I put together a complete guide covering the 10 most common anti-patterns that are killing test suites, and more importantly, exactly how to fix them. This isn't theory. These are the actual issues I see in code reviews, client projects, and conversations with QA engineers every week. Inside you'll find: • The hidden cost of hard-coded waits (and what to use instead) • Why tests that "work most of the time" are worse than no tests • How poor naming conventions cost you hours during incident response • The assertion mistake that makes tests pass when they shouldn't • Plus 6 more patterns with concrete fixes you can implement this week Each anti-pattern includes real examples, why it's problematic, the better approach, and a quick win you can tackle immediately. You don't have to fix everything at once. Pick the one causing your team the most pain and start there.

I used to feel stuck 🥵 when my automation tests failed—until I developed a structured 😎debugging approach! One of the biggest challenges in automation testing isn’t just writing test scripts—it’s debugging failures efficiently. Instead of blindly rerunning tests, I use a step-by-step approach to find and fix issues faster. 🔹 Here’s My Debugging Strategy: ✅ 1. Check the Error Logs & Stack Trace • Instead of guessing, I start by analyzing error messages, logs, and stack traces to identify the root cause. ✅ 2. Use Playwright’s Trace Viewer or Selenium Debugger • Playwright’s trace viewer helps me replay test execution, inspect elements, and identify failures visually. • In Selenium, I use breakpoints & debugging mode in IntelliJ/Eclipse. ✅ 3. Validate Locators & Page Load Timing • Many failures happen due to unstable locators or timing issues. I use auto-waiting & explicit waits instead of fixed sleeps. ✅ 4. Isolate & Reproduce the Issue • I run the failing test separately to check if it’s a script issue or an application bug. ✅ 5. Handle Flaky Tests Smartly • If a test passes sometimes but fails randomly, I use retries, improved locators, and network mocking to stabilize it. 💡 Pro Tip: Debugging is an essential skill in automation! Instead of rerunning failed tests, analyze and fix the root cause. 📌 How do you debug automation test failures? Let’s discuss in the comments, or connect with me for guidance here: https://lnkd.in/dUYHp9Af #AutomationTesting #Debugging #SoftwareTesting #Playwright #CareerGrowth

Inconsistent and intermittent failures tend to first reproduce long after the first time a check which exercises their code path is executed. This makes it almost impossible to know if a failure is from a new change, or from something that has been in the code for a long time. We had this problem in high volume in Microsoft Office due to an extremely large suite of end-to-end automated checks and a large number of shared tests. For years, the first place where intermittent failures would first appear would be when a developer ran their code assessing whether or not they had broken something prior to checking in. Roughly 85% of the time, developers after investigating the nature of the bug, determined it had nothing to do with their changes (I investigated this myself once - and I concurred, they were almost always correct in that assessment, and yes, that 85% is real). We flipped this trend by running all of the automation in the check-in suite many times per build. We would launch low-priority jobs on schedule against whatever the current build was consuming any unused capacity in the lab. Weekend builds typically got about 200 or so iterations, weekday between 50 and 100. The result was that most of the time by a wide margin, the first instance of an intermittent failure would come from these repeated runs. The system kept track so that when a developer saw the failure the report would indicate the issue was a known problem that had been in the code long before their changes were introduced. Meanwhile, these failures were tracked, investigated, and fixed mostly in order of frequency. We also had tooling that ran in the background trying different run and configuration parameters to try to increase frequency of hit and would notify product team engineers if it seemed like the bots were narrowing in on a repro condition. #softwaretesting #softwaredevelopment #embracetheflake

So today I wanted to share something that might help someone preparing for automation tester roles. An interview question I’ve been asked before, and my practical way of answering it. Interviewer asks: If your existing automation framework starts slowing down, becomes flaky, and test execution time is increasing with every sprint, how do you diagnose and fix it? If I were sitting in front of the interviewer, I’d answer it like an actual conversation. I’d say something like: First, I never assume the framework is the root issue. I start by looking at the failure patterns. If failures are random, I treat them as stability issues. If they’re consistent, I treat them as functional changes. I’d check the locator strategies, wait conditions, and whether the UI under test has changed without the automation being updated. Then I’d explain how I narrow it down. I usually rerun the failing tests in isolation to see if they pass on a clean run. If they do, the problem is usually around dependencies, test data, or improper cleanup. If they still fail, I dig into logs, screenshots, and network calls because sometimes,that's where the issues are. And I’d close my answer by showing how I fix the long-term problem. Most of the time, slowness means the framework isn’t evolving with the product. I look at whether we need parallel execution, better wait logic, or a refactor of common utilities. Whenever I introduce fixes, I run them against the entire suite to make sure the solution improves stability and not just a single test. If someone asked me that question today, that’s exactly how I’d answer it—practical, experience-based, and focused on root-cause thinking, not just tools. did i miss anything? Let me know your a swer to this question? #Automationtesting #sdets #testautomation