How to Use LLMs for Code Review Automation

Описание и применение

Автоматический LLM‑ревьюер, интегрированный в CI/CD, быстро выявляет стилистические и безопасностные ошибки, позволяя разработчикам сосредоточиться на архитектуре.

Задача

Slow, manual code review cycles delay merges and expose hidden anti‑patterns.

Результат

The workflow delivers a first‑pass code review that flags low‑level issues, enabling human reviewers to focus on higher‑level concerns and accelerating merge times.

Рабочий процесс

• Install openai and gitpython dependencies
• Create a plain‑text file with team rules (e.g., rate limiting, error handling, no secrets)
• Write a Python script that: 1) extracts the git diff, 2) loads rules, 3) constructs a prompt with context and diff, 4) calls the LLM with a JSON response format, 5) prints or posts the review results
• Integrate the script into a CI/CD pipeline (GitHub Actions or GitLab CI) so it runs automatically on PR creation or push

Доказательства

LLMs perform consistently better when they understand the code’s intended functionality

Источник

The most effective pattern is a hybrid process combining human involvement and LLMs

Источник