Continuous Integration and Continuous Delivery (CI/CD) pipelines are essential to modern software development. They promise quick, reliable, repeatable building and deployment of artifacts. However, they can be vulnerable to the pain points of even the best team or product, risky code, brittle tests, manual reviews and gates, a sprawling code base...
In this blog, we’ll discuss some practical ways to leverage AI tools to streamline your CI/CD process, from automating repetitive tasks (code review, purging zombie branches, updating trackers) to improving quality gates and providing human-friendly explanations when things fail.
One of AI’s most immediate benefits in any area is automating repetitive, error-prone tasks. CI/CD is no exception! Here are some areas where we humans tend to spin our wheels, or delay/defer our efforts, which can grind the CI/CD process to a halt:
AI models like What The Diff and CodeAnt AI can read (or even write) a pull request, summarize intent, flag risky changes (e.g., security-sensitive edits, large dependency bumps), and identify style or API contract violations. This reduces the cognitive load for reviewers and shortens review cycles.
ML models such as SonarCloud and Snyk can detect semantic linting and anti-patterns (e.g., incorrect concurrency patterns or misuse of cryptography libraries) by learning from previous code reviews and issue history. In many cases, they can also suggest the fix or even patch the code for you.
We’ve all branched code for features that went absolutely nowhere; and then they live on for months or years. Git tools like git-branch-clean can identify branches that have gone stale (no recent commits, no associated open PRs) which can be safely pruned.
Oh, and remember that feature you built back in 2019 that was going to change the world? You don’t? Well, it’s still being built and deployed every time a new commit arrives. Tools like NDepend and Roslynator in the .NET world, and ts-prune and unimported in the Node.JS world, can help detect code that is no longer used and can safely be deleted. This helps lower the cost of ownership of a code base over the long haul, as zombie code no longer needs to be maintained.
Most CI/CD tools and workflow tools offer out-of-the-box integrations that allow them to talk to each other. Your repository can inform your workflow app when a branch is created or PR is opened/completed; your pipeline can act as a quality gate on a PR, even commenting details like why a build failed or a score was below a threshold. Pipelines can even update ticket statuses and assign to releases as work progresses. If the out-of-the-box features aren’t refined enough, you may be able to tap into generic webhooks to automate in a more granular and detailed manner.
Another way AI can help update is by generating release notes. Jira’s AI assistant does a pretty great job of summarizing PRs into human-readable notes, even highlighting the changes that could impact users. You can also bring your own tool to the pipeline, like ReleaseNotes.dev.
We’ve all heard the horror stories of a profitable site that gets hijacked because of a years-old FOSS dependency that was compromised and never patched. Use a tool like Snyk or Dependabot to alert you when dependencies present an attack vector; Dependabot can even open a PR with the patched version for you, greatly easing the path toward accepting a patch.
As the old saying goes, tests don’t prove the absence of bugs; they can only prove the existence of them. But if your code isn’t covered by tests, they can do neither! Lucky for us, there are some great AI-powered tools which can suggest or even write unit tests based on existing code, like JetBrains AI Assistant and Cloving. And for TDD, apps like TestHatch and Autify can transform your user story or wireframe into test cases before you write your first line of code.
Every project has at least that one test that always fails at the worst time, preventing your pipeline’s gate from succeeding, and ultimately your hotfix from reaching production. (Usually this happens on Friday afternoons!) Trunk.io helps you know if it’s really safe to ignore a test or not. And when you are in a position to improve that flaky test, add a thread fuzzer or noise generator like Shaker or Undo AI to really put it through its paces.
There is an emerging field of integrated CI/CD chatbots which promises to add hours back to the average lifespan of an SRE. Instead of copying and pasting cryptic error messages into Google, what if you could ask a bot “Why did my build fail?” or “When did the test suite start running for longer than 10 minutes?” That’s exactly what CircleCI’s MCP Server and LogSage allow you to do! Or you can wire up your own RAG with access to source code and logs. Either way, the LLM can delve way deeper and way faster into this treasure trove of information than we could ever hope to. Th
A smooth and well-function CI/CD process can make or break the developer experience on any team or product. When your process is slow, manual, error-prone, or brittle, it can demand a lot of attention and sap the team’s energy. By introducing AI and automation, you can streamline the process and allow engineers to focus their efforts on what really excites us – building cool new features.