CI/CD pipeline guides — integrate automated testing into your deployment pipeline and ship with confidence.
Testing
Compliance testing done manually, at audit time, is expensive and unreliable. Controls that worked in Q1 can break in Q3 when someone updates a middleware or changes an API. Automated compliance testing shifts the catch point to when code changes — cheap to fix — rather than when auditors arrive. This guide
Testing
Catching accessibility regressions in CI is straightforward once you pick the right tools and decide what to block on. The challenge is not technical — it's deciding your enforcement strategy: block on any violation, block on regressions from baseline, or track and report without blocking. This guide covers the
Unity
Running Unity tests locally is fine. Running them automatically on every pull request is how you actually catch regressions before they ship. Setting up Unity CI/CD is notoriously painful because of licensing, but GameCI makes it manageable. This guide covers a production-ready Unity testing pipeline using GitHub Actions
Chaos Engineering
Most teams run chaos experiments manually — a team gathers, runs an experiment, watches dashboards, and documents findings. This works, but it doesn't scale. Manual chaos experiments happen infrequently, require coordination, and catch problems after code has already been deployed to production. Chaos engineering in CI/CD changes this.
Shift-Left Testing
Shift-left testing changes where quality lives in your delivery pipeline. Instead of discovering defects in staging or production, you find them in pull requests, local builds, and feature branches. CI/CD is the engine that makes this work at scale. This guide covers exactly how to wire shift-left
Testing
Webhook failures in CI are some of the most frustrating bugs to debug. The test passes locally but fails in CI. The webhook arrives but nothing happens. Logs show 200 OK but the feature doesn't work. This guide gives you a systematic approach for diagnosing and fixing webhook
CI/CD
The Dagger Python SDK lets you write CI pipelines as ordinary Python functions. No YAML, no DSL — just Python. You can import Dagger functions like any library, test them with pytest, and run them locally with the same command that runs in CI. This guide focuses on practical patterns: running
CI/CD
Dagger.io flips the CI/CD model on its head: instead of YAML pipelines that are impossible to test locally, you write your pipelines in Python, TypeScript, or Go — and run them anywhere. The same pipeline runs on your laptop, in GitHub Actions, and in GitLab CI without modification. This
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
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