Teaching an AI Agent to Debug Flaky Tests | The IntelliJ IDEA Blog

The text discusses leveraging AI Agent Skills to address the challenge of debugging flaky tests, which are tests that inconsistently pass or fail without changes to the code or test itself, thereby wasting resources and complicating bug identification. Using a Spring Boot project as an example, the article details a method of using AI, traditional developer tools, and coverage analysis to identify the root cause of flakiness by comparing code execution paths from successful and failing test runs. By modifying IntelliJ IDEA's coverage tool to provide detailed execution counts, developers can pinpoint the exact lines where execution diverges, indicating the source of the test's unreliability. The process is then packaged into a reusable AI Skill, allowing automated detection and resolution of flaky tests, demonstrating the broader potential of AI Skills in simplifying complex programming problems through structured, repeatable procedures.