AgentCodeReview: Implementation and Comprehensive Benchmark Evaluation of a Multi-Agent Framework for Explainable Code Review and Automated Bug Repair

Abstract

Large Language Models (LLMs) have revolutionized software development, from analyzing code and generating suggestions to detecting bugs and errors, and even creating entire programs. Despite these advances, existing AI-driven code review solutions still provide a one-size-fits-all approach to code review with overall feedback and suggestions, often of a non-specific nature. This restriction promotes modular architectures which would be able to provide specific and direct code quality reports. This paper presents the AgentCodeReview system, a multi-agent system that is able to conduct explainable code review and automated bug repair by leveraging software engineering agents with different code review tasks. There would be five independent entities, each one to be able to review code, analyze security, evaluate performance, document it and be able to automatically fix bugs. They run parallelly under the guidance of a centralized orchestration layer that collects the results from the analytical agents, calculates software quality scores and creates comprehensive HTML and PDF reports. Moreover, a Streamlit-based web interface was created that allows the interactive visualization of the results of the analysis and interactive entry of the input values. A set of twenty python programs was created to test the framework for effectiveness, consisting of a variety of runtime errors, security flaws, performance issues, documentation issues and a mixture of these types of errors. Two metrics, namely execution time and qualitative assessment were used to compare the proposed multi-agent framework with a single-agent framework as baseline. Experimental results demonstrated the benchmark execution success rate was 95%, while the multi-agent architecture provided more structured, explainable and domain specific feedback than the single agent. The extra computational cost of the coordinated analyses was acceptable for software quality assessment tasks because of the resulting interpretability and modularity. Through implementation and experiments, the results demonstrate AgentCodeReview's utility and extensibility to the field of explainable AI in software quality assurance. The proposed architecture can be expanded to other programming languages, integrated into the industrial development flow, and enhanced with the advanced LLMs for scalable intelligent code review.