# Code Linting using Language Models

arXiv: https://arxiv.org/abs/2406.19508 · HTML: https://mlsanity.com/paper/2406.19508/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [MetaLint: Easy-to-Hard Generalization for Code Linting](https://mlsanity.com/paper/2507.11687/) (2025)

## Abstract (arXiv)

Code linters play a crucial role in developing high-quality software systems by detecting potential problems (e.g., memory leaks) in the source code of systems. Despite their benefits, code linters are often language-specific, focused on certain types of issues, and prone to false positives in the interest of speed. This paper investigates whether large language models can be used to develop a more versatile code linter. Such a linter is expected to be language-independent, cover a variety of issue types, and maintain high speed. To achieve this, we collected a large dataset of code snippets and their associated issues. We then selected a language model and trained two classifiers based on the collected datasets. The first is a binary classifier that detects if the code has issues, and the second is a multi-label classifier that identifies the types of issues. Through extensive experimental studies, we demonstrated that the developed large language model-based linter can achieve an accuracy of 84.9% for the binary classifier and 83.6% for the multi-label classifier.