Evaluating Multi-Agent AI Systems for Automated Bug Detection and Code Refactoring

Abstract

This paper evaluates multi-agent AI systems for automating software bug detection and code refactoring. We design a cooperative architecture in which specialized agents—static-analysis, test-generation, root-cause, and refactoring—coordinate via a planning agent to propose, verify, and apply patches. The system integrates LLM-based reasoning with conventional program analysis to reduce false positives and preserve behavioral equivalence. We implement a reference pipeline on open-source Python/Java projects and compare against single-agent and non-LLM baselines. Results indicate higher fix precision and refactoring quality, with reduced developer review time, especially on multi-file defects and design-smell cleanups. We report ablations on agent roles, verification depth, and communication cost, and discuss failure modes (spec ambiguities, over-refactoring, flaky tests). A reproducible workflow, dataflow diagram, and flowcharts are provided to support replication. Our findings suggest that disciplined, verifiable agent orchestration is a practical path to safer, more scalable automated maintenance in modern codebases.

Introduction

Modern software evolves rapidly, accumulating bugs and code smells despite testing. Existing tools like linters and single-agent LLMs help, but they often produce unreliable or excessive edits, leading to low trust in automated changes.

This paper proposes a multi-agent system for automated software maintenance, where specialized agents (for bug detection, root-cause analysis, patching, refactoring, testing, and verification) collaborate under a Planner Agent. Each agent is tool-grounded, relying on concrete evidence like test failures or linter output, and changes are only accepted if verified by a final Verifier/Gatekeeper agent.

The system mimics human workflows by decomposing tasks, enforcing structured communication (via diffs, diagnostics, etc.), and emphasizing minimal, auditable, and semantically safe edits.

Key contributions include:

1. A multi-agent architecture with evidence-based collaboration.

2. Integration of LLM reasoning with static and dynamic analysis tools.

3. Empirical evaluation on real and synthetic bugs in Python and Java projects.

Results show:

• Higher fix precision than single-agent or static tools (e.g., 71% vs. 58% on Defects4J).

• Better refactoring quality, with 12–15% improvements in maintainability metrics.

• Fewer verification cycles, averaging 2.1 vs. 3.8 for single agents.

• Reduced review effort, with smaller diffs and clearer justifications.

Conclusion

This paper presented and evaluated a multi-agent AI framework for automated bug detection and code refactoring. Unlike monolithic assistants that attempt repair in a single step, our approach decomposes tasks into specialized agents—Planner, Bug Detector, Root-Cause, Refactoring, and Verifier—coordinated through a structured memory and messaging layer. The framework emphasizes evidence-grounded reasoning, requiring agents to cite compiler diagnostics, test results, or linter reports before changes are approved. Experimental results across benchmark datasets demonstrated that the multi-agent framework consistently outperforms single-agent LLM repairers and traditional static APR tools. It achieved higher bug fix precision, smaller and more maintainable patches, fewer verification iterations, and measurable reductions in developer review effort. These findings indicate that structured orchestration and role specialization can be more effective than simply scaling model size for software engineering tasks. By integrating refactoring into the bug-fixing pipeline, the framework also addresses a longstanding challenge: improving maintainability while preserving correctness. This dual focus strengthens the potential for adoption in real-world continuous integration workflows, where safety and developer trust are critical.

References

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Copyright

Copyright © 2025 Tanveer Aamina, Mohammed Zaid, Syeda Huda. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.