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AI-powered Code Review with LLMs: Early Results

Zeeshan Rasheed, Malik Abdul Sami, Muhammad Waseem, Kai-Kristian Kemell, Xiaofeng Wang, Anh Nguyen, Kari Systä, Pekka Abrahamsson

arXiv:2404.18496v2cs.SE

TL;DR

Code review and optimization remain underexplored, and traditional processes and static analysis often provide limited actionable feedback. The paper proposes a four-agent LLM system for issue detection and improvement suggestions; its preliminary evaluation reports autonomous review and actionable feedback across agent roles, with code-quality and developer-learning benefits stated as aims.

  • Problem

    LLM use in code review and optimization remains underexplored, while traditional review and static analysis often lack deep, actionable feedback for identifying issues and suggesting improvements.

    Source: §1 · p. 2

  • Method

    The paper proposes a four-agent LLM system that reviews code for issues and provides actionable recommendations for improvement.

    Source: §1 · p. 2, §3.1 · p. 4

  • Results

    The preliminary evaluation reports autonomous code review and logical, actionable feedback across all agent roles, including detection of issues that static analysis tools missed or explained only briefly in several cases.

    Source: §4.1 · p. 5

  • Takeaways & Limitations

    The authors conclude that these agents can improve traditional code review while supporting code quality and developer education.

    Source: §6 · p. 6

Abstract

from arXiv · show

In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

1. Introduction

LLM-based code review and optimization remain underexplored, while traditional review and static analysis often provide limited actionable feedback. The paper proposes a four-agent system for identifying issues and suggesting improvements, with future work aimed at evaluating documentation updates and broader technical-debt detection.

1. Introduction

  • Code review and optimization remain underexplored, and traditional processes and static analysis often lack actionable feedback beyond syntax errors or known bug patterns.The stated gap includes the lack of an LLM-based model that identifies issues, suggests optimizations, and educates developers on best practices.Source: §1 · p. 2
  • The paper introduces a four-agent LLM system that identifies code smells, potential bugs, and standards deviations while suggesting improvements for code quality and developer learning.The authors distinguish its proactive code optimization from traditional static analysis.Source: §1 · p. 2
  • Future work proposes empirical comparison of agent-generated documentation updates with manual methods and extension to technical debt across code, design, architecture, testing, documentation, build, and infrastructure.The study would involve developers testing the system across projects from various domains.Source: §1 · p. 2
  • The listed contributions emphasize a multi-agent code-review system with actionable improvements and proactive code optimization.The authors contrast this focus with traditional static analysis tools.Source: §1 · p. 2

2. Related Work

Prior work automates several code-review tasks, including reviewer assignment, comment recommendation, and code modification, while static analysis targets coding-standard violations and common errors. The section identifies a gap in comprehensive LLM tools combining detailed review, best-practice documentation, issue detection, and optimization.

2. Related Work

  • Code review takes substantial reviewer time and effort, and research has addressed reviewer allocation, comment recommendations, and code quality.The cited work includes recommending suitable reviewers, and review comments.Source: §2 · p. 2
  • Prior work reports 30% of code reviews face reviewer-assignment challenges, recommends reviewers by file location or history, predicts code-change approval, retrieves review suggestions, and modifies code to meet review-comment specifications.Source: §2 · p. 3
  • Static analysis instruments have been advocated for automatically detecting coding-standard violations and prevalent errors,.Source: §2 · p. 3
  • The authors identify a lack of comprehensive LLM tools that combine detailed code review, best-practice documentation, code-smell and bug detection, standards checks, and optimization.They propose an LLM-based agent model that identifies bugs and offers suggestions and recommendations.Source: §2 · p. 3

3. Research Method

The method frames code-review assistance as identifying potential issues and offering actionable recommendations. It implements this as four specialized agents that analyze code and pass findings through a multi-agent workflow.

3. Research Method

  • The research question asks how an LLM-based agent can identify potential code issues and offer actionable review recommendations.The method motivates this question by noting limits in traditional processes and tools' depth of insight and optimization feedback.Source: §3 · p. 4

3.1. LLM-assisted Code Review (RQ1)

  • The system uses four specialized agents—Code Review, Bug Report, Code Smell, and Code Optimization—to analyze repositories, identify issues, and suggest improvements.Source: §3.1 · p. 4
  • The Code Review Agent performs an initial assessment for bugs, code smells, and standards deviations, then forwards its findings for deeper analysis.Source: §3.1 · p. 4
  • After the initial review, the Bug Report Agent uses preliminary findings to focus its analysis on patterns and anomalies associated with software bugs.Source: §3.1 · p. 4
  • The Code Smell Agent evaluates design after earlier assessments and proposes refactoring strategies intended to improve long-term quality and reduce technical debt.Source: §3.1 · pp. 4–5
  • Building on earlier agents, the Code Optimization Agent recommends algorithm refinement, redundancy reduction, efficiency improvements, and code restructuring.It can produce optimized code while preserving intended functionality.Source: §3.1 · p. 5

4. Preliminary Result

The preliminary evaluation reports that the multi-agent system can review code autonomously and produce actionable feedback across its agent roles. Findings describe issue detection, design-oriented refactoring, and code modifications targeting readability, redundancy, and performance.

4. Preliminary Result

  • The preliminary evaluation reports autonomous code review and logical, actionable feedback across all agent roles, with coordinated agents producing consistent outcomes.The section characterizes findings as spanning minor bugs to significant code smells and inefficiencies across programming languages and AI application domains.Source: §4.1 · pp. 5–6, §4 · p. 5

4.1. LLM-Based AI Agent Results (RQ1)

  • The Code Review Agent identified structural issues and summarized potential risks, while the Bug Detection Agent found logical inconsistencies, possible runtime errors, and weak code structures.In several cases, bug findings were missed or only minimally explained by traditional static analysis tools; the authors say LLM reasoning can complement rule-based approaches with context-aware insights.Source: §4.1 · p. 5
  • The Code Smell Agent recognized common anti-patterns and stylistic concerns, with refactoring suggestions addressing naming, function decomposition, and architectural clarity.The authors report that suggestions were generally aligned with accepted software-engineering principles.Source: §4.1 · p. 5
  • The Code Optimization Agent proposed changes targeting readability, redundancy, and performance, and generated optimized code segments while preserving functionality in several test cases.Source: §4.1 · p. 5

5. Future Work

Future work will evaluate the system against manual methods, investigate its educational impact, and extend it to identify broader forms of technical debt.

5. Future Work

  • The planned empirical study will compare generated documentation with manual methods for accuracy and efficiency, using developer discussions, code-smell and bug-report analysis, and best-practice documentation.The study also aims to assess whether the system can streamline software development.Source: §5 · p. 6
  • The authors plan to investigate whether actionable code-improvement feedback can advance developer knowledge and adherence to best practices.The educational impact remains a subject for future investigation.Source: §5 · p. 6
  • The system may be extended to identify technical debt across code, design, architecture, testing, documentation, build, and infrastructure.Source: §5 · p. 6

6. Conclusions

The paper concludes that four specialized agents improve code review by identifying potential issues and offering optimization recommendations. It presents code quality and developer education as complementary benefits, while proposing comparative documentation evaluation as future work.

6. Conclusions

  • The authors report that four specialized agents substantially improve identification of potential code issues and provide actionable optimization recommendations.Source: §6 · p. 6
  • The findings describe a dual benefit of improved code quality and developer education through detecting code smells, suggesting optimizations, and promoting best practices.Source: §6 · p. 6
  • Future work will compare LLM-generated documentation with manual methods to examine the system’s potential to streamline software development practices.Source: §6 · p. 7
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