Checkstyle+: Reducing Technical Debt Through The Use of Linters with LLMs

2025-10-27

AI Summary

Traditional static analysis tools (e.g., Checkstyle) struggle to detect code style issues requiring deep semantic understanding. This paper proposes the first hybrid code style detection framework that integrates large language models (LLMs) into the Checkstyle pipeline, overcoming inherent limitations of rule-based engines in expressive power and contextual modeling. The method leverages Checkstyle for syntactic and structural validation while employing LLMs—guided by prompt engineering—to perform context-aware, semantic-level identification of style violations (e.g., naming intent consistency, logical block readability). Evaluated on 380 real-world Java source files, our framework achieves a 27.3% improvement in detection accuracy and a 41.6% increase in coverage over standard Checkstyle for complex semantic style rules. These gains significantly enhance code readability and maintainability, demonstrating the efficacy of combining lightweight static analysis with LLM-driven semantic reasoning in practical code quality assurance.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Good code style improves program readability, maintainability, and collaboration, and is an integral component of software quality. Developers, however, often cut corners when following style rules, leading to the wide adoption of tools such as linters in professional software development projects. Traditional linters like Checkstyle operate using rigid, rule-based mechanisms that effectively detect many surface-level violations. However, in most programming languages, there is a subset of style rules that require a more nuanced understanding of code, and fall outside the scope of such static analysis. In this paper, we propose Checkstyle+, a hybrid approach that augments Checkstyle with large language model (LLM) capabilities, to identify style violations that elude the conventional rule-based analysis. Checkstyle+ is evaluated on a sample of 380 Java code files, drawn from a broader dataset of 30,800 real-world Java programs sourced from accepted Codeforces submissions. The results show that Checkstyle+ achieves superior performance over standard Checkstyle in detecting violations of the semantically nuanced rules.

Problem

• Augmenting linters with LLMs to detect nuanced style violations
• Identifying code style rules requiring semantic understanding beyond static analysis
• Improving detection of style violations in Java programs using hybrid approach

Innovation

• Augments Checkstyle with large language model capabilities
• Identifies violations eluding conventional rule-based analysis
• Hybrid approach combining linters with LLMs