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

arXiv: https://arxiv.org/abs/2602.07783 · HTML: https://mlsanity.com/paper/2602.07783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 71 references that are in the map.
- First posted on arXiv 8 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
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- [LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs](https://mlsanity.com/paper/2406.15319/) (2024)
- [Program Synthesis using Natural Language](https://mlsanity.com/paper/1509.00413/) (2015)
- [Learning Natural Coding Conventions](https://mlsanity.com/paper/1402.4182/) (2014)
- [Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds](https://mlsanity.com/paper/2305.17590/) (2023)
- [Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach](https://mlsanity.com/paper/2406.16386/) (2024)
- [CXXCrafter: An LLM-Based Agent for Automated C/C++ Open Source Software Building](https://mlsanity.com/paper/2505.21069/) (2025)
- [A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects](https://mlsanity.com/paper/2007.08978/) (2020)
- [Automating a Complete Software Test Process Using LLMs: An Automotive Case Study](https://mlsanity.com/paper/2502.04008/) (2025)
- [STYLE-ANALYZER: fixing code style inconsistencies with interpretable unsupervised algorithms](https://mlsanity.com/paper/1904.00935/) (2019)
- [Natural Language Commanding via Program Synthesis](https://mlsanity.com/paper/2306.03460/) (2023)
- [Automated Unit Test Refactoring](https://mlsanity.com/paper/2409.16739/) (2024)
- [Styler: learning formatting conventions to repair Checkstyle violations](https://mlsanity.com/paper/1904.01754/) (2019)
- [Refactoring to Pythonic Idioms: A Hybrid Knowledge-Driven Approach Leveraging Large Language Models](https://mlsanity.com/paper/2406.03660/) (2024)
- [MLinter: Learning Coding Practices from Examples-Dream or Reality?](https://mlsanity.com/paper/2301.10082/) (2023)

## Abstract (arXiv)

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.