Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards

2026-02-08

AI Summary

Manually configuring linters requires expert knowledge and struggles to adapt across multiple programming languages, coding standards, and tooling ecosystems, leading to high maintenance overhead. This work proposes LintCFG, the first approach to apply compiler design principles to automated linter configuration generation. It introduces a tool-agnostic domain-specific language (DSL) to structurally encode coding rules and leverages large language models to automatically compile natural language specifications into concrete linter configurations, enabling end-to-end automation across languages, standards, and tools. Evaluated on Java Checkstyle tasks, the DSL achieves over 90% precision and recall in rule representation, with fine-grained configuration generation exceeding 70% accuracy—more than doubling the performance of baseline methods. User studies confirm significant gains in developer productivity, and the approach successfully generalizes to JavaScript ESLint scenarios.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Coding standards are essential for maintaining consistent and high-quality code across teams and projects. Linters help developers enforce these standards by detecting code violations. However, manual linter configuration is complex and expertise-intensive, and the diversity and evolution of programming languages, coding standards, and linters lead to repetitive and maintenance-intensive configuration work. To reduce manual effort, we propose LintCFG, a domain-specific language (DSL)-driven, LLM-based compilation approach to automate linter configuration generation for coding standards, independent of programming languages, coding standards, and linters. Inspired by compiler design, we first design a DSL to express coding rules in a tool-agnostic, structured, readable, and precise manner. Then, we build linter configurations into DSL configuration instructions. For a given natural language coding standard, the compilation process parses it into DSL coding standards, matches them with the DSL configuration instructions to set configuration names, option names and values, verifies consistency between the standards and configurations, and finally generates linter-specific configurations. Experiments with Checkstyle for Java coding standard show that our approach achieves over 90% precision and recall in DSL representation, with accuracy, precision, recall, and F1-scores close to 70% (with some exceeding 70%) in fine-grained linter configuration generation. Notably, our approach outperforms baselines by over 100% in precision. A user study further shows that our approach improves developers'efficiency in configuring linters for coding standards. Finally, we demonstrate the generality of the approach by generating ESLint configurations for JavaScript coding standards, showcasing its broad applicability across other programming languages, coding standards, and linters.

Problem

• linter configuration
• coding standards
• manual effort
• tool diversity
• configuration maintenance

Innovation

• DSL
• LLM
• linter configuration
• coding standards
• automated compilation