Source-linked AI summary
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
TL;DR
Code review and static-analysis tools are described as limited in actionable feedback, and the paper proposes a repository-trained LLM agent framework with four specialized review agents. The preliminary results report issue detection and tailored recommendations across AI projects, while the authors identify documentation-update comparisons with manual methods as future work.
Problem
The paper identifies limited actionable feedback in traditional code review and static analysis, alongside a lack of a purpose-built LLM model for issue detection, optimization suggestions, and developer education.
Source: §1 · p. 2Method
The proposed LLM-based framework uses four specialized agents for code review, bug reports, code smells, and code optimization, trained on repository material including reviews, bug reports, and best-practices documentation.
Source: §1 · p. 2, §3.1 · p. 4Results
Takeaways & Limitations
The authors conclude that the agents can augment traditional code review while supporting code quality and developer education.
Source: §6 · p. 8
Abstract
from arXiv · showhide
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
The introduction frames LLM-assisted code review as an underexplored gap: existing review and static-analysis approaches offer limited actionable feedback, while the proposed agent combines issue detection, optimization suggestions, and developer education. It also outlines future evaluation of generated documentation updates against manual methods.
1. Introduction
- Code review and static-analysis tools are described as offering limited actionable feedback, while a purpose-built LLM model for issue detection, optimization, and developer education is missing.The paper identifies code review as a means of finding bugs, applying coding standards, and sharing knowledge.Source: §1 · p. 2
- The proposed agent is trained on code repositories, reviews, bug reports, and best-practices documentation to identify smells, bugs, and standards deviations and suggest improvements.The stated goals include code quality and developer education.Source: §1 · p. 2
- Future work will compare the accuracy and efficiency of LLM-generated documentation updates with manual methods.The planned study includes manually conducted reviews, best-practice documentation, and developer discussions.Source: §1 · p. 2
- The approach is positioned as proactive code optimization, in contrast to traditional static-analysis tools.The contribution summary emphasizes proactive optimization and actionable improvements.Source: §1 · p. 2
2. Related Work
Prior work automates several stages of code review, including reviewer assignment, comment recommendation, and code-quality improvement. The paper identifies a remaining gap in comprehensive LLM-based review that also documents best practices and recommends code improvements.
2. Related Work
- Code-review automation covers reviewer recommendation, comment locations and content, and code-quality improvement.The paper describes code review as effort- and time-intensive for reviewers,.Source: §2 · p. 3
- Prior work recommends reviewers by file location or review history, predicts approval using CNN/LSTM and Transformer, retrieves comment-informed reviews, and modifies code to meet review-comment specifications.Static-analysis instruments have been advocated to identify coding-standard violations and common errors,.Source: §2 · p. 3
- The paper identifies an absence of a comprehensive LLM tool combining detailed reviews, best-practice documentation, bug and smell detection, and optimization suggestions.Existing code-review platforms are described as offering similar core features with minimal automation.Source: §2 · p. 3
3. Research Method
The method investigates how an LLM-based agent can identify code issues and provide actionable recommendations. It organizes code-review work into four specialized agents trained using repository material and aimed at distinct review and optimization tasks.
3. Research Method
- The research question asks how an LLM-based agent can identify potential code issues and offer actionable recommendations in code reviews.The motivation is the limited depth and actionability of traditional review processes and tools.Source: §3 · p. 4
3.1. LLM-assisted Code Review (RQ1)
- The proposed approach uses four specialized agents: Code Review, Bug Report, Code Smell, and Code Optimization.Each agent handles a distinct aspect of code review by analyzing repositories, identifying issues, or suggesting improvements.Source: §3.1 · p. 4
- Training material includes code repositories, historical reviews, bug reports, and best-practices documentation accessed through GitHub’s REST API.The method also describes obtaining code, commits, issues, and pull requests.Source: §3.1 · p. 4
- The Code Review and Bug Report agents identify potential code issues and bugs, while the Code Smell agent detects anti-patterns and suggests refactoring.The Code Smell agent targets patterns that may create maintenance or scalability challenges.Source: §3.1 · pp. 4–5
- The Code Optimization Agent recommends and applies improvements, drawing on examples of efficient and inefficient code across contexts and languages.Its stated targets include execution speed, memory use, and maintainability.Source: §3.1 · p. 5
4. Preliminary Result
The preliminary results describe the multi-agent model as identifying bugs, code smells, and inefficiencies across varied programming languages and AI application domains. Project examples show tailored findings and recommendations, from parsing failures to performance and design concerns.
4. Preliminary Result
- The model is reported to identify issues ranging from minor bugs to significant code smells and inefficiencies across programming languages and AI application domains.The results describe autonomous code review for bugs, smells, and code-optimization suggestions.Source: §4 · p. 6
4.1. LLM-Based AI Agent Results (RQ1)
- DeepDive’s Unicode parsing failure prompted a handling recommendation and function refactoring, while NeuroStartUp’s hard-coded training parameters prompted configurable options.The recommendations target parsing robustness, maintainability, and adaptation to different datasets.Source: §4.1 · p. 6
- VisionQuest received a recommendation for newer segmentation algorithms, while LinguaKit’s large-corpus bottlenecks prompted parallel processing and more efficient data structures.The stated targets include processing speed and accuracy for VisionQuest, and throughput and memory use for LinguaKit.Source: §4.1 · pp. 6–7
- AIFriendly’s weak error handling, QuantumLeap’s global variables and unclear names, and BioNexus’s inefficient training loop each received targeted improvement suggestions.Recommendations included detailed error reporting, refactoring state and naming, and more efficient batch processing with GPU acceleration where possible.Source: §4.1 · p. 7
- EcoSim received a vectorized-operations suggestion for simulation calculations, RoboTutor a recommendation for dynamically adapting learning paths, and SafeRoute a more frequently updated caching strategy.The cases address algorithmic inefficiency, rigid decision trees, and outdated cached information, respectively.Source: §4.1 · p. 7
5. Future Work
Future work will compare the accuracy and efficiency of model-generated documentation with manual methods through empirical study. It will also examine developer education and whether automation could streamline development.
5. Future Work
- The empirical study will analyze developer discussions and code reviews for code smells and bug reports, alongside best-practice documentation.Source: §5 · pp. 7–8
- The planned study will assess whether model-generated documentation is accurate and more efficiently produced than manual efforts.The evaluation is intended to be quantitative and qualitative.Source: §5 · p. 8
- The authors anticipate that automating documentation and code-review tasks could reduce developer time spent on them and free time for core development.Source: §5 · p. 8
- The authors plan to investigate whether actionable code-improvement feedback can advance developer knowledge and adherence to best practices.Source: §5 · p. 8
6. Conclusions
The paper presents a four-agent LLM framework for code review and reports improved issue identification and actionable optimization recommendations. It concludes that the approach may support code quality and developer education, while planned work will compare generated documentation with manual methods.
6. Conclusions
- The four-agent LLM framework substantially improved identification of potential code issues and provided actionable optimization recommendations.Source: §6 · p. 8
- The authors report that the agents can augment traditional reviews by enhancing code quality and developer education, with positive developer feedback on their recommendations.Source: §6 · p. 8
- Planned future work will compare LLM-generated documentation with manual methods to examine its potential to streamline software development.Source: §6 · p. 8
- The paper frames the approach's potential as extending from technical improvements to greater knowledge and skills across the developer community.Source: §6 · pp. 8–9