Spec-Driven Refactoring: What I Learned from Handing an 800-Line Java Monolith to AI

说明与用途

实验表明，在使用 AI 进行遗留代码重构时，先明确行为需求和约束，再让 AI 提出实现方案更为有效。这样既提升了代码结构和质量，也强调了严格测试的重要性。

问题

Refactor a legacy 800‑line Java monolith without breaking existing behaviour while improving design and maintainability.

结果

The spec‑driven approach enabled meaningful refactoring with AI, preserving behaviour, improving code quality, and revealing that clear problem definition is more valuable than design details when delegating to AI.

工作流

• 1. Define behavioural baseline with Gherkin/Cucumber tests.
• 2. Run SonarQube for static quality assessment.
• 3. Create .spec/refactoring specifications outlining goal, current implementation, desired implementation, behaviour to preserve, and acceptance criteria.
• 4. Use GitHub Copilot in auto mode to generate refactorings based on specifications.
• 5. Verify behaviour with existing tests and SonarQube metrics.
• 6. Iterate, refining specifications and reviewing AI output.

证据

I created a .spec folder at the root of the project and a refactoring section inside it.

来源

I used SonarQube to assess the code. This gave me a second perspective on the refactoring: not just whether the application still worked, but whether the code itself had improved.

来源