# Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements

arXiv: https://arxiv.org/abs/2506.10330 · HTML: https://mlsanity.com/paper/2506.10330/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://mlsanity.com/paper/2404.08144/) (2024)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [Review of Large Vision Models and Visual Prompt Engineering](https://mlsanity.com/paper/2307.00855/) (2023)
- [LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2308.11148/) (2023)
- [AI-powered Code Review with LLMs: Early Results](https://mlsanity.com/paper/2404.18496/) (2024)
- [Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation](https://mlsanity.com/paper/2406.12114/) (2024)
- [Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions](https://mlsanity.com/paper/2404.09135/) (2024)
- [Exploring the Capabilities of LLMs for Code Change Related Tasks](https://mlsanity.com/paper/2407.02824/) (2024)
- [LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward](https://mlsanity.com/paper/2401.03374/) (2024)
- [What Makes a Code Review Useful to OpenDev Developers? An Empirical Investigation](https://mlsanity.com/paper/2302.11686/) (2023)
- [A Systematic Literature Review and Taxonomy of Modern Code Review](https://mlsanity.com/paper/2103.08777/) (2021)
- [VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs](https://mlsanity.com/paper/2408.04125/) (2024)
- [Sorald: Automatic Patch Suggestions for SonarQube Static Analysis Violations](https://mlsanity.com/paper/2103.12033/) (2021)
- [Combining static analysis and dynamic symbolic execution in a toolchain to detect fault injection vulnerabilities](https://mlsanity.com/paper/2303.03999/) (2023)

## Built on by (most cited first, 2 of 2)

- [The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase](https://mlsanity.com/paper/2603.25697/) (2026)
- [LLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering](https://mlsanity.com/paper/2605.02455/) (2026)

## Abstract (arXiv)

This study examined code issue detection and revision automation by integrating Large Language Models (LLMs) such as OpenAI's GPT-3.5 Turbo and GPT-4o into software development workflows. A static code analysis framework detects issues such as bugs, vulnerabilities, and code smells within a large-scale software project. Detailed information on each issue was extracted and organized to facilitate automated code revision using LLMs. An iterative prompt engineering process is applied to ensure that prompts are structured to produce accurate and organized outputs aligned with the project requirements. Retrieval-augmented generation (RAG) is implemented to enhance the relevance and precision of the revisions, enabling LLM to access and integrate real-time external knowledge. The issue of LLM hallucinations - where the model generates plausible but incorrect outputs - is addressed by a custom-built "Code Comparison App," which identifies and corrects erroneous changes before applying them to the codebase. Subsequent scans using the static code analysis framework revealed a significant reduction in code issues, demonstrating the effectiveness of combining LLMs, static analysis, and RAG to improve code quality, streamline the software development process, and reduce time and resource expenditure.