CI/CD pipeline guides — integrate automated testing into your deployment pipeline and ship with confidence.
CI/CD
Both Dagger and GitHub Actions run your tests in CI. The difference is how you define and debug those pipelines. This guide compares them on the things that matter when you're running test suites: local execution, caching, services, parallelism, and debugging when something breaks. The Core Difference GitHub
CI/CD
The Dagger TypeScript SDK lets you write CI pipelines in TypeScript — the same language as your application. Your pipeline is type-safe, unit-testable, and runs identically on your laptop and in GitHub Actions. This guide focuses on Node.js and Next.js testing workflows: running Vitest, Jest, Playwright, and integrating with
CI/CD
Integration tests that hit real databases and message queues are harder to run than unit tests. In GitHub Actions, you configure service containers in YAML and hope they start before your tests run. In Dagger, you define services as code, bind them to your test container, and get the same
AI Testing
Deploying an LLM is not a one-time event. Prompts change. Models get updated. Retrieval indexes get refreshed. Each of these changes can silently degrade the quality of your AI application — and without a continuous evaluation pipeline, you won't know until users start complaining. This guide covers how to
Testing
A deployment pipeline without smoke tests is like a fire sprinkler system with no water pressure test. Everything looks fine until the moment you need it. Smoke tests in CI/CD serve one purpose: stop bad builds before they cause damage. If the app doesn't boot, a critical
AI Testing
Agentic testing means AI agents that autonomously handle steps in the testing cycle — creating tests, running them, analyzing failures, and fixing issues — without waiting for explicit human instruction at each step. This guide covers the patterns, tools, and implementation approach for teams adopting agentic testing workflows in 2026. Key Takeaways
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 the
Testing
Review apps — a dedicated deployment for every pull request — are one of the most effective ways to catch bugs before they reach your main branch. Instead of reviewing code in a diff, reviewers interact with the actual running application. Instead of waiting for tests to run against a shared staging
Test Reliability
Flaky tests are hard to detect manually — a test that fails 5% of the time looks like noise until someone tracks it across 20 CI runs. GitHub Actions gives you tools to surface flaky tests automatically: test annotations that appear directly on pull requests, workflow features for re-running failed jobs,
Testing
End-to-end tests are slow by nature — they launch browsers, navigate real pages, wait for network. A 400-test Playwright suite can take 20-30 minutes on a single runner. Sharding splits those tests across multiple workers and brings that down to 5-8 minutes. Here's how to configure it on GitHub
Testing
A test suite that takes 15 minutes to run is a test suite developers avoid running. The fix isn't always parallelism — often it's finding the 5% of tests consuming 60% of the time and making them faster. Here's a systematic approach to profiling and
Testing
JUnit XML is the universal language of CI/CD test reporting. Originally designed for Java's JUnit, almost every test framework can emit it — Jest, pytest, Playwright, Vitest, PHPUnit, and hundreds more. Every major CI platform knows how to parse it. Here's how to use JUnit XML