Second, combine high quality checks into your pipeline. Static evaluation, linting, and safety scanning needs to be non-negotiable components of steady integration every time AI code is launched. Many steady integration/steady supply (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, you must begin leveraging AI for testing, not simply coding. AI may also help write unit exams and even generate take a look at knowledge. For instance, GitHub Copilot can help in drafting unit exams for capabilities, and devoted instruments like Diffblue Cowl can bulk-generate exams 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 take a look at instances, then run them routinely.
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, contemplate licensing implications; make sure that any AI-derived code complies together with your code licensing insurance policies to keep away from authorized complications.
