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

Описание и применение

Эксперимент показал, что при рефакторинге старого кода с помощью ИИ важно сначала определить поведенческие требования, а затем дать ИИ свободу выбора реализации. Такой подход улучшил структуру кода и качество, но потребовал тщательного тестирования.

Задача

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

Результат

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.

Рабочий процесс

• 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.

Доказательства

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

Источник

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.

Источник