An Empirical Study on the Effectiveness of Iterative LLM-Based Improvements for Static Analysis Issues

Maintaining and evolving software systems often demands more effort than their initial development. Improving source code quality through automated support can significantly reduce technical debt, increase maintainability, and enhance developer productivity. This paper presents an experimental approach that integrates static analysis with Large Language Models (LLMs) to automate source code improvement. The proposed pipeline iteratively processes Java classes by extracting issues detected by SonarQube and transforming them into prompts for LLMs, which generate improved code versions. Each version is reanalyzed, and the process repeats until convergence or a predefined iteration limit is reached. The experimental setup includes multiple configurations combining two LLMs (GPT-4-mini and Gemini), variation in temperature, prompt style, and number of iterations. Evaluations were conducted using multiple Java datasets, with three repeated runs for the Commons Lang repository to identify behavioral patterns. The analysis focuses on the number of issues reduction, decrease in technical debt (measured in a SonarQube metric), and the evolution of issue severity. Functional correctness was assessed manually by inspecting and executing the improved code to ensure behavior preservation. The results demonstrate that combining SonarQube with LLMs is effective in reducing code issues—achieving over 58% average reduction in key scenarios—while preserving functionality. The iterative process proved successful in guiding the models to incrementally improve code quality based on real static analysis feedback. This work contributes a reproducible and extensible pipeline, offering insights into the impact of LLM configurations and supporting further research in the integration of AI and software quality engineering.