Analysis of Python linters reveals rule overlap and performance differences
Understanding Python Linters through a Comprehensive and Systematic Empirical Study
Software Engineering
Summary
Code linters are tools that help programmers find and fix mistakes faster. The authors studied five popular Python linters to see how their checks, speed, and settings compare. They found many rules are shared among linters, especially Ruff and Pylint, with Ruff being the fastest. They also looked at how people configure and combine linters in real projects. This study helps users pick and set up linters better and supports future tool improvements.
What this means in practice
- •For python developers: Choose the right combination of linters and configurations to improve code quality with minimal delay during development.
- •For tool builders: Build faster and more comprehensive linters or automated configuration tools guided by rule coverage and performance benchmarks.
Authors
Qingyun Yang, Zejun Zhang, Dapeng Yan, Tian Zhang, Zhenchang Xing, Yi Li, Zhiming Liu
Abstract
Code linters, tools that automatically detect code issues, help developers accelerate development and improve code quality by identifying errors early. Unlike previous studies that focused mainly on single linters or informal comparisons, this paper presents the first large-scale empirical study of five widely used Python linters: Ruff, Pylint, Flake8, Mypy and Black, through four key user concerns: linting rule checks, speed, configuration methods, and linter preferences. We analyze 1,551 linting rules, categorize them into five types, and construct rule mappings, finding that more than 40% are shared between multiple linters, with Ruff and Pylint together covering over 80% of all rules. Pylint has the most rules in the first two categories, and Ruff leads in the last three. Next, we measure their running times on 1,000 GitHub projects under three setups (single-rule, default, all-rules), finding a consistent speed ranking from fastest to slowest: Ruff, Mypy, Flake8, Black, and Pylint. Ruff is extremely fast, with runtimes typically within one second. Then, we summarize four configuration methods and four types of configuration options from 443 available options across the five linters. While all linters support the four configuration methods, they differ in project configuration formats and flexibility in their configuration options. Finally, an analysis of 3,094 GitHub projects using the five linters reveals that over 50% use multiple linters, share strong consensus on option selections and rule suppressions, but exhibit project-specific variations in enabled-rule configurations. Our findings provide actionable insights for linter users and establish a benchmark dataset and taxonomy to support future research, such as cross-linter rule mapping, performance modeling, and automated configuration.