CI/CD pipeline
Continuous integration and continuous delivery describe two habits: merging code changes frequently rather than in large batches, and keeping every change ready to release rather than saving up work for a big deployment. A CI/CD pipeline is the concrete automation that makes both habits actually happen. Instead of a person manually building, testing, and deploying a change, the pipeline runs those steps automatically, in a fixed order, every time someone pushes code.
A typical pipeline starts the moment a change is pushed or a pull request opens. It builds the code, compiling it or installing dependencies to produce something runnable. It runs tests against that build, often unit tests first since they're fastest, followed by integration or regression tests that take longer. If everything passes, the pipeline can deploy the result, sometimes straight to production, sometimes to a staging environment first, and sometimes gated behind a manual approval step before it goes any further.
That last stage is where continuous delivery and continuous deployment part ways. Continuous delivery means every change that clears the pipeline is ready to ship, but a person still decides when to actually release it. Continuous deployment removes that decision and releases automatically once a change passes every check. Tools like GitHub Actions, Azure DevOps, and Jenkins are common ways teams actually define and run these pipelines, usually as a configuration file that lives in the same repository as the code it builds.
Typical stages of a pipeline
- Trigger, a push, a pull request, or a schedule that starts a pipeline run.
- Build, compiling the code or installing dependencies to produce a runnable artifact.
- Test, running unit, integration, and often regression tests against that build.
- Deploy, pushing the artifact to staging or production, sometimes behind a manual approval gate.
- Notify, reporting the result back to the team so a failure gets noticed quickly.
Where it shows up
- A pull request triggers a build-and-test run before a merge is allowed to happen.
- Passing tests unlock an automatic deployment to a staging environment for further checks.
- A tagged release triggers a production deployment gated behind a manual approval step.
- A nightly pipeline runs a fuller regression suite than what runs on every commit.
Benefits and challenges
| Benefit | Challenge |
|---|---|
| Catches a broken build or a failing test within minutes of a change, not days later. | A pipeline is only as trustworthy as the tests running inside it, weak coverage lets a broken change through green. |
| Makes deploying a routine, repeatable action instead of a manual, error-prone event. | A slow pipeline becomes something a team routes around instead of respecting, blocking rather than helping the flow of changes. |
| Gives every change the same consistent checks, regardless of who wrote it. | Flaky tests inside the pipeline erode trust fast, and a team that starts ignoring red pipelines has lost the point of having one. |
Frequently asked questions
What's the difference between continuous delivery and continuous deployment?
Continuous delivery means every change that passes the pipeline is ready to release, but a person still decides when to release it. Continuous deployment goes one step further and releases automatically once a change clears the pipeline, with no manual approval step in between.
What tools run a CI/CD pipeline?
GitHub Actions and Azure DevOps are common choices when code is already hosted on GitHub or in Azure, while Jenkins remains widely used where a team wants more control over how the pipeline itself is hosted and configured.
Does a pipeline replace manual testing?
No. A pipeline automates the checks that make sense to run on every change, mainly builds and automated tests like unit and regression testing. Exploratory and usability testing still tend to need a person, a pipeline just makes sure the automated groundwork happens consistently first.
How Klarent helps
Klarent's tests run as a step inside a CI/CD pipeline like any other check, and self-healing execution keeps them passing through interface changes so a pipeline doesn't have to slow down to wait on broken automation.