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Outline of software development. Continuous integration and continuous delivery explained". Retrieved Continuous Integration, Delivery, and Deployment. Packt Publishing. ISBN S2CID Categories : Software development. Hidden categories: Articles with short description Short description is different from Wikidata.
Namespaces Article Talk. Views Read Edit View history. Help Learn to edit Community portal Recent changes Upload file. Download as PDF Printable version. Supporting disciplines Configuration management Documentation Software quality assurance Project management User experience. Another step you may want to consider is integrating your CI server with your code review tool so that each commit must pass code review before it can be committed, but only after it has been successfully built and tested.
Enforcing these extra layers of business logic at the start of the process helps to keep your codebase clean and ready for release, while minimizing interruptions and delays in the pipeline. Your build agents, however, do the heavy lifting of running builds and executing tests according to instructions received from the CI server.
When you use your CI server to configure the logic for a stage of your pipeline, you can specify a range of details and rules. For example, you may want to run certain tests on commits to the master branch but not when running a pre-commit build on a development branch, or you may want to control how many builds can call a test database at the same time.
Being able to run certain tasks at the same time using different build agents can make your pipeline more efficient. In the latter case, setting up a composite build will aggregate the results so you can treat the tasks as a single build step. A CI server that integrates with cloud-hosted infrastructure, such as AWS, will allow you to benefit from elastic, scalable resources in which to run your builds and tests.
If your infrastructure needs are considerable, support for containerized build agents and integration with Kubernetes will allow you to manage your build resources efficiently, whether they are in the cloud or on-premises. Your CI server should allow you to configure various failure conditions , which it will then apply and use to determine the status of a particular step and whether to proceed to the next stage of the pipeline. In addition to self-evident failures, such as a build returning an error code or tests failing to execute, you can define other types of failure based on data collected by your CI server.
Examples include test coverage decreasing relative to the previous build indicating that tests have not been added for the latest code changes , or the number of ignored tests increasing compared to the last successful build. These metrics serve as a useful warning that code quality may be deteriorating.
By triggering a failure for these reasons and limiting which users have permission to override these failures, you can drive desirable behavior. Having produced your build artifacts and run an initial set of tests during the CI phase, the next step is to deploy those artifacts to QA environments for further levels of testing, followed by staging so that your stakeholders can try it out, and then — if everything looks good — release to live.
As well as providing an artifact repository to store the outputs from each build so you can deploy them as needed, a CI server can also store and manage parameters for each environment in your pipeline.
You can then specify whether your deployment scripts are triggered automatically based on the outcome from the previous stage.