Naohide Anahara
I work as an engineer at Tokyo Gas Co., Ltd.
Tokyo Gas is a very old company with a 140-year history, but we are currently in the process of advancing DX by promoting in-house system development.
Session
This proposal introduces an innovative approach to Python code quality enforcement by combining fixit (a linting framework based on libcst) with generative AI to create custom linters tailored to team-specific coding standards.
Traditional linters like ruff provide general-purpose rules but struggle to address organization-specific requirements and coding conventions. This creates challenges where code review becomes subjective and dependent on individual reviewers' knowledge. Our solution leverages AI to generate fixit rules from natural language descriptions, dramatically reducing the barrier to creating and maintaining custom linting rules.
The core innovation lies in using libcst's Concrete Syntax Tree (CST) representation, which preserves formatting, comments, and whitespace—unlike traditional Abstract Syntax Trees (AST). This enables safe, automated code transformations that maintain the original code's style while enforcing new standards. By combining AI-assisted rule generation with fixit's powerful transformation capabilities, teams can quickly implement and enforce new coding standards across entire codebases, eliminating review subjectivity and accelerating modernization efforts.