Testing
React component testing has evolved considerably. Enzyme snapshots gave way to React Testing Library, and the community's view on snapshot testing shifted along with it. The current reality: snapshots aren't obsolete, but their role is more specific than it once was. Understanding what they're
Testing
There's a pattern that plays out on almost every team that adopts snapshot testing. It starts with good intentions: snapshots are easy to create, they run automatically, and they seem to provide coverage. Then a few months later, someone's blocked on a PR review because of
Testing
Snapshot testing is one of those features that sounds like a silver bullet — write one line of code, and Jest will catch any unexpected change in your output. In practice, it works brilliantly for some scenarios and creates noise in others. Understanding the mechanism helps you decide when to reach
Testing
Bugs found in production cost 10 times more to fix than bugs found during development. That figure, cited in IBM's Systems Sciences Institute research, has shaped how high-performing engineering teams think about quality. The answer is simple in concept, hard in practice: find defects earlier. That is
Testing
The most realistic test environment is production. Staging environments, no matter how carefully maintained, diverge from production over time. Real user traffic patterns are different from synthetic load tests. Real data has shapes that test fixtures do not replicate. Real infrastructure behaves differently at scale. Feature flags and dark launches
Testing
Security vulnerabilities have a compounding cost curve that makes testing late particularly painful. A SQL injection found in a code review takes an hour to fix. The same vulnerability found after a breach means forensic investigation, regulatory notifications, customer remediation, legal exposure, and reputational damage that no SLA covers. Shift-
Test Reliability
Flaky tests cost CI time and erode confidence. Playwright gives you several tools to manage flakiness: automatic retries that reduce false negatives, --repeat-each for detecting flakiness before it hits CI, and test.fail() for acknowledging known issues without deleting coverage. This post covers all three strategies — when to use
Testing
PIT (PITest) is the standard mutation testing tool for Java. It's fast, integrates cleanly with Maven and Gradle, and generates detailed HTML reports that overlay mutation results directly onto your source code. Here's how to get it running and use the output effectively. Why PIT Over
Testing
Mutation testing is valuable. It's also slow. A medium-sized Java project can take 45 minutes to run PIT against every class. A TypeScript monorepo with Stryker can run for hours. This is why most teams that try mutation testing in CI give up after a week. But
Test Reliability
When a flaky test blocks your CI pipeline, you have three bad options: delete it (lose the coverage), skip it (test.skip, forget it forever), or fix it immediately (takes time you don't have). The quarantine pattern is the fourth option: isolate it, run it separately, and track
Test Reliability
A flaky test is a test that passes sometimes and fails sometimes without any change to the code. It fails in CI, you re-run it, it passes. You ship. Three days later it fails again. Nobody changed anything relevant. Flaky tests are expensive. They erode trust in your test
Testing
You've run your first mutation testing report. Mutation score: 58%. Now what? Improving mutation score isn't about writing more tests — it's about writing better assertions. Here's a systematic approach to turning surviving mutants into killed ones, starting with the changes that matter