No regression: what it means for feature flag experiments

Ever rolled out a new feature only to find it broke something else? We've all been there. Navigating the world of software development can be tricky, especially when trying to balance new features with existing functionalities.

That's where non-regression testing and feature flags come into play. They're powerful tools that help us introduce new features without disrupting what already works. Let's dive into how these strategies can be your best friends in seamless feature rollouts.

Understanding non-regression testing in feature flag experiments

Non-regression testing might sound fancy, but at its core, it's all about making sure new features don't mess up what already works. When we're using feature flags to roll out updates, it's crucial to focus on the areas these flags impact. This way, we can catch bugs early and fix issues before they become big problems. It's like having a safety net that enhances software quality and stability during those exciting new releases.

So how do we integrate non-regression testing with feature flags? It starts by setting up a stable benchmark release—think of it as our "control" version. Then we define test routines for the parts of the software affected by the feature flags. Running these tests on both the benchmark and the new release helps us spot any discrepancies. And when we automate these tests within our CI/CD pipelines, we get continuous feedback that keeps us on top of software quality throughout the whole experimentation process.

Of course, there are challenges—like incomplete test coverage and limited resources. But by maintaining a comprehensive test suite and prioritizing test cases based on risk and impact, we can overcome these hurdles. Throw in some exploratory testing alongside automated tests, and we're in good shape. Adopting these strategies means our feature flag experiments deliver valuable insights without rocking the boat of existing features.

At Statsig, we believe understanding the no regression meaning is essential here. We're verifying that new features work as intended without introducing bugs or breaking old functionalities. Focusing on the directly impacted areas saves time and resources—we catch potential issues early, and software quality stays tip-top throughout the process.

The practice of inverting feature flags in testing

Have you ever thought about flipping your feature flags in tests? By setting them to "on" instead of "off," we can catch breaking changes during continuous integration (CI). This idea, which sparked some interesting discussions on Reddit, ensures our tests stay relevant by checking features in their intended active state. Plus, it helps us remove feature flags promptly after rollout, cutting down on technical debt.

I know it might go against the grain—traditionally, we default feature flags to "off" in tests. But inverting them actually maintains test suite integrity. It's all about maximizing the match between test code paths and production code paths. This approach helps us spot unintended behavioral changes and prevents our test coverage from degrading over time.

By defaulting flags to "on" in end-to-end tests, we avoid those nasty surprises where tests pass under current production flag values but fail when the feature is fully rolled out. This way, engineers can proactively address any test changes as new functionality is introduced. It makes for a much smoother rollout process.

Of course, to make this work, we need to think carefully about when to use feature flags versus experiments. Feature flags are great for gradual rollouts and measuring impact, while experiments are all about testing hypotheses between different product variants. Combining both tools lets us run complex, segmented tests with controlled target audiences.

Managing code complexity with feature flags

Feature flags are awesome—they give us control over how we roll out features. But let's be honest: they can also clutter up our code and increase complexity if we're not careful. As more flags pile up, the codebase gets harder to navigate and maintain, and that can introduce new bugs. So, managing feature flags effectively is crucial to keep our code quality and readability in check.

One good practice is to regularly review and remove obsolete feature flags. Think of it like cleaning out the closet—get rid of what's no longer needed to keep things tidy. Automated tools can help identify unused flags, and having clear naming conventions and documentation makes flag management a breeze. By establishing guidelines for how we use and remove flags, we ensure everyone on the team is on the same page.

It's all about balancing the benefits of feature flags with their impact on code complexity. If we strategically use flags for critical features and promptly remove them after rollout, we can minimize code clutter. Regular code refactoring and investing in flag management tools and processes also go a long way toward maintaining a healthy codebase.

At the end of the day, effective feature flag management lets us reap the benefits without sacrificing code quality. By following best practices and utilizing the right tools, like those offered by Statsig, we can control feature rollouts while keeping our code clean and maintainable. This leads to faster iteration, reduced risk, and overall better software quality.

Best practices for integrating non-regression testing and feature flags

So, how do we tie it all together? Establishing clear objectives for non-regression testing is key to maintaining software quality. By defining a comprehensive test suite that covers critical functionalities and those pesky edge cases, we ensure thorough testing coverage.

Automating these non-regression tests within our CI/CD pipelines gives us continuous feedback on software stability. This approach catches potential issues early, allowing for quick fixes and preventing technical debt from piling up.

When deploying new features using feature flags, it's essential to prioritize test cases based on risk and impact. Focusing on high-risk areas and critical user journeys ensures a smoother rollout.

Using feature flags for targeted testing lets us gradually expose new functionality to users. This minimizes the impact of potential issues and enables quick rollbacks if necessary—a strategy discussed in this Reddit thread.

Remember how we talked about inverting feature flags in tests? As explained by Samsara Engineering, it keeps our tests relevant and prevents the test suite from degrading over time. And don't forget to regularly review and remove obsolete feature flags—it reduces code complexity and keeps our test suite in good shape.

Closing thoughts

Bringing it all together, non-regression testing and feature flags are powerful allies in releasing new features smoothly without disrupting existing functionalities. By integrating best practices like inverting feature flags in tests and managing code complexity, we maintain software quality and speed up delivery.

At Statsig, we understand the challenges of feature rollouts and testing. Our tools can help you implement these strategies effectively. Feel free to check out our resources or reach out to learn more. Hope you found this helpful!