Benefits of Testing in Production

How Big Tech Tests in Production Without Breaking Everything  Most outages happen because changes weren’t tested under real-world conditions before deployment.  Big tech companies don’t gamble with production.  Instead, they use Testing in Production (TiP)—a strategy that ensures new features and infrastructure work before they go live for all users.  Let’s break down how it works.  1/ Shadow Testing (Dark Launching) This is the safest way to test in production without affecting real users.  # How it works:  - Incoming live traffic is mirrored to a shadow environment that runs the new version of the system.   - The shadow system processes requests but doesn’t return responses to actual users.   - Engineers compare outputs from old vs. new systems to detect regressions before deployment.  # Why is this powerful?  - It validates performance, correctness, and scalability with real-world traffic patterns.   - No risk of breaking the user experience while testing.   - Helps uncover unexpected edge cases before rollout.  2/ Synthetic Load Testing – Simulating Real-World Usage  Sometimes, using real user traffic isn’t feasible due to privacy regulations or data sensitivity.  Instead, engineers generate synthetic requests that mimic real-world usage patterns.  # How it works:   - Scripted requests are sent to production-like environments to simulate actual user interactions.   - Engineers analyze response times, bottlenecks, and potential crashes under heavy load.   - Helps answer:     - How does the system perform under high concurrency?     - Can it handle sudden traffic spikes?     - Are there any memory leaks or slowdowns over time?  🔹 Example: Netflix generates synthetic traffic to test how its recommendation engine scales during peak usage.   3/ Feature Flags & Gradual Rollouts – Controlled Risk Management  The worst thing you can do? Deploy a feature to all users at once and hope it works.  Big tech companies avoid this by using feature flags and staged rollouts.  # How it works:   - New features are rolled out to a small percentage of users first (1% → 10% → 50% → 100%).   - Engineers monitor error rates, performance, and feedback.   - If something goes wrong, they can immediately roll back without affecting everyone.  # Why is this powerful?   - Minimizes risk—only a fraction of users are affected if a bug is found.   - Engineers get real-world validation in a controlled way.   - Allows A/B testing to compare the impact of new vs. old behavior.  🔹 Example:   - Facebook uses feature flags to release new UI updates to a limited user group first.   - If engagement drops or errors spike, they disable the feature instantly.  Would you rather catch a bug before or after it takes down your system?

WAIT... before you grab your pitchforks, let’s clear this up: Testing in production ≠ shipping untested code. It means validating 𝗮𝗹𝗿𝗲𝗮𝗱𝘆-𝘃𝗲𝘁𝘁𝗲𝗱 𝗰𝗼𝗱𝗲 in the real world. Production is where edge cases live. It's where your real users are. It’s where you get real answers. Canary releases to reduce the blast radius. Feature flags to toggle functionality safely. Shadow traffic to test without touching user experience. Load + chaos testing to simulate the unpredictable. They’re the only way to catch real-world issues that test environments will never expose. Yes, it’s scary. Yes, it’s necessary. No, it doesn’t mean throwing untested code into the wild. It means you trust your pre-prod coverage enough to validate assumptions in real conditions... with guardrails. Done right, testing in prod closes the feedback loop, exposes blind spots, and prevents surprises during peak traffic (especially when combined with powerful automation). The future of QA isn't “test, ship, pray.” It’s test smarter. Ship safer. Learn faster. #softwaretesting #qa #testautomation Video credit: codespotters

Only one test matters: "When we deliver this change, it should provide the expected value." That test can only be run in production. All the additional testing before production is only there to give us confidence that we are not breaking anything. - We've not created a security problem. - We've not degraded performance - The UX doesn't suck - We haven't broken existing behaviors - etc. We can never prove we've found every problem; we can only be relatively confident. The longer it takes us to gain that confidence, the more money it will cost, and the larger the batch will be. Larger batches make it harder to find problems and enable us to deliver more of the wrong thing. Even if we've not introduced a new problem and we build exactly the right thing, the delays and added costs still lower the value of the delivered batch. Measure your quality process from idea to delivery and reduce the cost and size of every delivery. This will reduce the number of failures of the only test that matters. Want tips? Check out Flow Engineering by Steve Pereira and Andrew Davis