A Study of Large Language Models in Detecting Python Code Violations

Adhering to good coding practices is critical for enhancing a software’s readability, maintainability, and reliability. Common static code analysis tools for Python, such as Pylint and Flake8, are widely used to enforce code quality by detecting coding violations without executing the code. Yet, they often fail to handle deeper semantic understanding and contextual reasoning. This study investigates the effectiveness of large language models (LLMs) compared to traditional static code analysis tools in detecting Python coding violations. Six state-of-the-art LLMs: ChatGPT, Gemini, Claude Sonnet, DeepSeek, Kimi, and Qwen are evaluated against Pylint and Flake8 tools. To do so, a curated dataset of 75 Python code snippets, annotated with 27 common code violations, is used. In addition, three common prompting strategies: Structural, chain-of-thought, and role-based, are used to instruct the selected LLMs. The experimental results reveal that Claude Sonnet achieved the highest F1-Score (0.81), outperforming Flake8 (0.79) and demonstrating strong precision (0.99) and recall (0.69). However, LLMs show differences in performance, with Qwen and DeepSeek underperforming relative to others. Moreover, LLMs that identified documentation and design violations (such as type hints and nested method structures) perform better than stylistic consistency and complex semantic reasoning. The results are heavily influenced by the prompting approach, with structural prompts yielding the most balanced performance in the majority of cases. This research contributes to the empirical work on employing LLMs for code quality assurance while also demonstrating their potential role as complementary static code analysis tools for Python, with methodologies that may extend to other languages.