How to Stabilize Flaky Tests in Legacy Code

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.

How I handled flaky date pickers with Playwright. One of the sources of flaky UI automation on a recent testing project was not authentication, not API timing, and not even dynamic tables. At first glance, the flow looked simple: click the input, open the calendar, move to the right month, pick the day, continue with the form. In practice, it kept failing in different ways depending on execution speed, browser state, environment load, and UI rendering timing. The root problem was that the original automation treated the date picker like a static visual widget, while in reality it behaved more like a dynamic client-side component with delayed rendering and state-dependent controls. What caused the flakiness before the fix: • the script clicked visible day cells before the calendar finished rendering • month navigation buttons became interactable earlier than the calendar state actually updated • identical day numbers appeared from previous and next months, so the locator sometimes clicked the wrong date • the component occasionally re-rendered after navigation, which detached elements and broke the click chain The fix was not adding more waits. The fix was changing the strategy. Instead of automating the calendar as a series of blind clicks, I handled it based on the real implementation of the control. The improved approach looked like this: • first identify whether the field was a native input or a custom calendar widget • whenever direct input was allowed, use fill() with the expected format instead of forcing calendar interaction • for custom pickers, wait for the correct month and year state before selecting any day • scope locators to the active calendar container so duplicated dates outside the current month were ignored • validate the final input value after selection to confirm that the UI action produced the expected result After that change, the test stopped behaving like a visual guess and started behaving like a stable user flow with state verification. That one adjustment reduced flaky failures noticeably and made the same scenario much more reliable in CI, which mattered because this date field was part of a business-critical booking flow. My biggest takeaway was simple: with date pickers, the problem is rarely “Playwright is unstable.” More often, the automation is interacting with the component at the wrong abstraction level. If the control is a real input - treat it like data entry. If the control is a rich UI widget - treat it like a stateful component. That is one of those small testing details that looks minor in code review but has a huge impact on regression stability, maintenance cost, and trust in the automation suite. #Playwright #UIAutomation #TestAutomation #QA #SoftwareTesting #E2ETesting #AutomationTesting #SDET #FlakyTests #QualityAssurance

🎯Flaky Tests Driving You Crazy? Here's How to Master CI-Only Failures We've all been there: tests pass locally ✅ but mysteriously fail in CI ❌ After debugging countless flaky tests, I've created a comprehensive demo project that tackles the most common culprits: The 5 Usual Suspects: ⚡ Race Conditions — When your clicks outpace state updates ⏱️ Async Timing Issues — Hard-coded waits that fail under CI latency 🔒 Test Isolation Problems — Leaked state haunting your test suite 🌐 Network Flakiness — Those unpredictable API timeouts 💻 Environment Dependencies — Viewport, timezone, and OS gotchas What I've Learned: The key isn't just fixing individual tests—it's understanding WHY they fail in CI: →Different CPU/memory resources →Network latency variations →Parallel execution exposing hidden race conditions →Environment differences (OS, timezone, viewport) Best Practices That Actually Work: → Intercept API calls instead of hard-coded waits → Ensure complete test isolation with proper cleanup → Stub external dependencies religiously → Make tests environment-agnostic from day one My Debugging Arsenal: I've built custom Cypress commands that save hours: ▸ cy.waitForStableDOM() — Wait for dynamic content to settle ▸ cy.captureState() — Snapshot app state for comparison ▸ cy.retryUntilSuccess() — Handle legitimate retries ▸ cy.smartWait() — Multi-strategy element waiting 🔑 The Game Changer? CI simulation locally. Run your tests with CI environment settings before pushing. Catch those failures before they hit your pipeline. For anyone battling flaky tests: you're not alone, and there ARE patterns to the madness. Understanding these 5 scenarios has transformed how I write tests. 📦 Check out the full demo project: 🔗 https://lnkd.in/gUJDezrC #SoftwareTesting #QualityAssurance #CypressCypress.io #CI #TestAutomation #DevOps #ContinuousIntegration #SoftwareDevelopment

📣 Day 65: Automating Smart Retry Logic in Selenium – Handling Flaky Tests with RetryAnalyzer, Re-Invokes & Adaptive Recovery! 🔁🔧 👨💻 #AutomationTestingSeries by Kishore Kumar D Flaky tests are the silent killers in automation suites. They pass sometimes, fail randomly, and waste hours of debugging — even though the app works fine. ✅ In real-time MNC projects, you’ll often hear: "It failed once… then passed in rerun." This is where Smart Retry Logic becomes a game-changer. 🔵 Why Smart Retry Logic Is a Must in Enterprise QA ✔️ Network Latency & Timeout Glitches ♦️ API call delayed? → Test fails before response arrives ♦️ Retry allows controlled second chance before marking failed ✔️ Dynamic UI & Asynchronous Rendering ♦️ Animations, loaders, and delays can cause stale elements ♦️ Retry helps if a transient error disappears on re-run ✔️ Third-Party Dependency Flakiness ♦️ Payment gateway, Captcha API, tracking scripts may cause 1-off failures ♦️ Retry ignores false negatives without skipping real issues ✔️ Parallel Execution Race Conditions ♦️ Shared data access or setup conflicts ♦️ Retry isolates these to detect repeatable vs. flaky 🧠 Real-Time QA Scenarios 🧾 Insurance App – Premium calculator test fails on first run, works fine on rerun 📄 Root cause: Slow API response → RetryAnalyzer saves manual reruns 🧾 Banking Portal – Transaction confirmation modal takes time to load 📄 First click fails → Retry helps stabilize automation 🧾 Retail Web App – Element not clickable due to spinner overlay 📄 Retry after 2 seconds → Click succeeds and test passes 🧾 Travel Site – Fare price fluctuates slightly on re-load 📄 Retry captures average fare correctly → avoids false fail 🛠️ Pro Tips for Implementation ✅ Use a custom RetryAnalyzer class with limits (e.g., 2 retries) ✅ Log retry attempts for traceability in reports ✅ Combine with smart wait conditions to avoid blind reruns ✅ Retry only on known transient failures (e.g., ElementNotInteractableException) ✅ In CI pipelines, rerun only failed tests using TestNG listeners or Maven surefire rerun plugin 🛑 Common Mistakes to Avoid ❌ Retry everything blindly → Hides real defects ❌ No logging on retries → Devs can't debug what failed ❌ Over-reliance on retry → Indicates flaky automation design ❌ Not differentiating between flaky and failed → Poor test suite quality 💬 Tomorrow’s Topic Preview: 📣 Day 66: Automating Test Resilience with Try-Catch & Custom Exception Handling in Selenium – Build Fault-Tolerant Test Scripts! 🛡️ #Selenium #RetryAnalyzer #FlakyTests #AutomationTestingSeries #SmartRecovery #TestNG #ResilienceInAutomation #AdaptiveRetry #Maven #RealTimeQA #KishoreKumarD #AutomationArchitectMindset #CIStability

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.

Your Playwright test passes locally. CI marks it failed. Every single time. Not a bug. Not flaky infrastructure. You're testing async code like it's synchronous. Here's what that means — and how to fix it ↓ Think of it like ordering food. SYNCHRONOUS (HTTP) You order at a counter. You wait there. They hand you food. You walk away. ASYNCHRONOUS (Events / Kafka) You order online. You get a confirmation instantly. Food arrives 30 minutes later. Same final result. Completely different timing. Why your async tests keep breaking Sync test logic: trigger → wait → assert ✓ Async test mistake: trigger event → assert immediately → FAIL The data isn't there yet. Your test arrived before the food did. This is why 80% of "flaky" tests aren't actually flaky. They're asserting too early. The fix in Playwright TypeScript Instead of asserting immediately after an async trigger: await expect(async () => {  expect(await getOrderStatus(orderId)).toBe('PROCESSED') }).toPass({ timeout: 30_000 }) Poll until the state changes. Then assert. One pattern. Eliminates most async test instability. The mental model for every test you write One question before you write any assertion: Does the caller need an answer to continue? YES → Synchronous. Assert right after the call. NO → Asynchronous. Poll, wait, then assert. Where most SDETs go wrong with async 1. Asserting too early — system still processing 2. Using setTimeout() as a fix — fragile, breaks in slow CI 3. Not testing the failure path — what if it never processes? 4. Ignoring the Dead Letter Queue — where failed events disappear silently Synchronous and asynchronous aren't just backend architecture. They directly determine whether your test suite is reliable or something your team quietly ignores. What's the most confusing async scenario you've run into? -x-x- To learn more such concepts using Playwright Typescript, Enrol into my course: https://lnkd.in/gHYidnfr #japneetsachdeva