Feature Flags - HelpMeTest

Testing

Shift-Left with Feature Flags and Dark Launches: Testing in Production Safely

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

Deployment Testing

Feature Flag Testing Best Practices: Control Risk in Production

Feature flags (also called feature toggles or feature switches) are one of the most powerful tools in a modern engineering team's toolkit. They decouple deployment from release, let you test features in production with controlled rollouts, and enable instant kill switches when something goes wrong. But feature flags

Testing

Gradual Rollouts and Canary Deployments: Testing Strategies for Progressive Delivery

Gradual rollouts and canary deployments reduce risk but introduce new testing problems: how do you test that the rollout percentage is correct, that monitoring detects regressions, and that rollback works before you need it in production? This guide covers testing strategies for the full progressive delivery lifecycle. What Makes Progressive

Testing

Testing Feature Flags with Flagsmith and Jest: Targeting Rules and Local Evaluation

Flagsmith supports local evaluation mode where flag rules are downloaded once and evaluated in-process. This makes it ideal for testing—no HTTP calls during test runs. This guide covers testing flag evaluation, targeting rules, percentage rollouts, and identity overrides in Jest without a running Flagsmith instance. Why Flagsmith Is

Testing

LaunchDarkly vs Statsig vs Unleash: Feature Flag Tools Compared (2026)

LaunchDarkly is the enterprise standard with the most complete SDK ecosystem. Statsig combines feature flags with built-in A/B experimentation and product analytics. Unleash is the open-source option you self-host. This comparison covers SDK quality, testing support, pricing model, and the tradeoffs for teams of different sizes.

Testing

A/B Testing in Production: Statistical Significance, Sample Size, and Rollout Testing

A/B testing infrastructure is code, and code needs tests. This guide covers testing the experiment layer itself: variant assignment consistency, statistical significance calculations, minimum detectable effect, sample size requirements, and percentage rollout correctness—not just whether the feature exists behind the flag. Testing the Test Infrastructure Most teams write