Second, combine high quality checks into your pipeline. Static evaluation, linting, and safety scanning must be non-negotiable elements of steady integration at any time when AI code is launched. Many steady integration/steady supply (CI/CD) instruments (Jenkins, GitHub Actions, GitLab CI, and so forth.) can run suites like SonarQube, ESLint, Bandit, or Snyk on every commit. Allow these checks for all code, particularly AI-generated snippets, to catch bugs early. As Sonar’s motto suggests, guarantee “all code, no matter origin, meets high quality and safety requirements” earlier than it merges.
Third, as lined above, it is best to begin leveraging AI for testing, not simply coding. AI can assist write unit exams and even generate check information. For instance, GitHub Copilot can help in drafting unit exams for features, and devoted instruments like Diffblue Cowl can bulk-generate exams for legacy code. This protects time and in addition forces AI-generated code to show itself. Undertake a mindset of “belief, however confirm.” If the AI writes a perform, have it additionally provide a handful of check circumstances, then run them robotically.
Fourth, in case your group hasn’t already, create a coverage on how builders ought to (and shouldn’t) use AI coding instruments. Outline acceptable use circumstances (boilerplate era, examples) and forbidden ones (dealing with delicate logic or secrets and techniques). Encourage builders to label or remark AI-generated code in pull requests. This helps reviewers know the place further scrutiny is required. Additionally, think about licensing implications; make sure that any AI-derived code complies along with your code licensing insurance policies to keep away from authorized complications.
