Chapter 4. Deploying to Test Environments
In Chapter 3, we explored the fundamentals of continuous integration, focusing on early steps in a CI/CD pipeline: mainly, building and pre-deployment testing. We walked through an example pipeline triggered when a PR is opened, as shown in Figure 4-1.
Figure 4-1. A CI pipeline
This pipeline built and packaged the code, conducted static code analysis, and executed early, fast tests including unit and lightweight integration tests, providing build and test feedback to the PR. These steps ensure that the code in the pull request is merge-ready, providing confidence that the merged code would function as intended and would not introduce any regressions. Assuming that the code changes in the PR prove ready, the developer can merge the PR.
With our new code merged, the next step is getting ready for production by deploying into test environments and then running a battery of tests. AI and ML tools are being integrated into the deployment process. These tools help teams make better deployment decisions, identify potential issues proactively, and streamline the verification process. Rather than adding complexity, well-implemented AI actually reduces the cognitive load on developers while improving deployment reliability.
Between the CI steps and the production release, we are primarily focused on testing. We want to learn if the release is ...
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