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Second, combine high quality checks into your pipeline. Static evaluation, linting, and safety scanning must be non-negotiable elements of steady integration each time AI code is launched. Many continuous integration/continuous delivery (CI/CD) instruments (Jenkins, GitHub Actions, GitLab CI, and so on.) 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 coated above, it is best to begin leveraging AI for testing, not simply coding. AI can assist write unit assessments and even generate check knowledge. For instance, GitHub Copilot can assist in drafting unit assessments for capabilities, and devoted instruments like Diffblue Cowl can bulk-generate assessments for legacy code. This protects time and likewise 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 instances, 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 instances (boilerplate technology, 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 additional scrutiny is required. Additionally, take into account licensing implications; be certain any AI-derived code complies along with your code licensing insurance policies to keep away from authorized complications.
