CI/CD pipeline guides — integrate automated testing into your deployment pipeline and ship with confidence.
Contract Testing
Contract testing only protects you if it runs in CI/CD. A manually run Pact verification that isn't blocking deploys is just documentation. This guide shows how to wire contract testing into a real CI/CD pipeline: consumer tests publish pacts on every build, providers verify on every
CI/CD
Trunk-based development (TBD) requires one thing above all else: main must always be deployable. A broken main blocks everyone. In a team of 10 developers committing multiple times per day, a broken main is catastrophic. Continuous testing is what makes TBD viable at scale. The question isn't
CI/CD
Traditional CI/CD testing stops at the deployment boundary. Tests run before production; production is where you find out if the tests were sufficient. This creates a one-way information flow: tests inform deployments, but production never informs tests. The result is systematic blind spots. Tests cover the paths engineers
AI Testing
CodeRabbit adds AI-powered code review to your pull requests. It comments on potential bugs, style violations, and security issues automatically. But here's the thing: AI code reviewers need to be tested too. If your review automation is generating noise, missing real issues, or slowing down merges, you
Codecov
Codecov's codecov.yml file controls thresholds, PR comments, coverage flags, carryforward settings, and ignore patterns. Without it, Codecov uses defaults that may not match your project's standards. This guide covers the most useful settings. Key Takeaways codecov.yml lives at the root of your repository. Codecov
Test Management
QMetry's value proposition depends heavily on how well it integrates with the rest of your toolchain. A test management tool that sits in isolation — where testers manually update results after CI runs — creates more work than it saves. This guide covers how to connect QMetry to real CI/
Codecov
Codecov and Coveralls both receive coverage reports from CI, display trends over time, and post PR comments showing coverage changes. Codecov offers more powerful PR analytics and supports more coverage formats. Coveralls is simpler and free for open source. This guide covers setting up both and integrating with GitHub. Running
Code Coverage
Effective CI coverage strategies enforce coverage on new code (patch coverage), gradually increase thresholds over time (ratchet), and fail builds only for meaningful drops—not arbitrary percentages. This guide covers threshold patterns, per-language implementations, and common anti-patterns to avoid. Coverage metrics are misused more often than used well.
CI/CD
Automated testing is only as good as the pipeline that runs it. You can have the best test suite in the world, but if it runs inconsistently, takes 45 minutes, or gets skipped under pressure, it provides no real safety net. Getting your CI/CD pipeline testing right is what
CI/CD
A large codebase accumulates tests the same way it accumulates technical debt — gradually, then suddenly. A test suite that took 3 minutes a year ago now takes 25 minutes, and nobody can pinpoint when it crossed the threshold from "fast enough" to "everyone waits for CI."
CI/CD
Microservices CI/CD testing is a different problem from monolith testing. In a monolith, you run all the tests against all the code. In microservices, each service has its own repository, its own pipeline, and its own deploy cadence — but it depends on other services it doesn't control.
CI/CD
CI/CD testing costs compound fast. A 20-minute test suite running on every push, across a team of 20 engineers, adds up to thousands of compute-hours per month. As coverage grows, build times grow. As build times grow, engineers wait, context-switch, and eventually start skipping tests. Cost