# AI-powered Code Review with LLMs: Early Results

arXiv: https://arxiv.org/abs/2404.18496 · HTML: https://mlsanity.com/paper/2404.18496/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2024.

## Built on by (most cited first, 22 of 22)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025)
- [Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness](https://mlsanity.com/paper/2408.04585/) (2024)
- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells](https://mlsanity.com/paper/2409.19801/) (2024)
- [CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generation](https://mlsanity.com/paper/2504.13472/) (2025)
- [RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation](https://mlsanity.com/paper/2503.22851/) (2025)
- [Automated Code Review Using Large Language Models at Ericsson: An Experience Report](https://mlsanity.com/paper/2507.19115/) (2025)
- [Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications](https://mlsanity.com/paper/2508.12358/) (2025)
- [Understanding Dominant Themes in Reviewing Agentic AI-authored Code](https://mlsanity.com/paper/2601.19287/) (2026)
- [Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement](https://mlsanity.com/paper/2603.00539/) (2026)
- [Code Review Agent Benchmark](https://mlsanity.com/paper/2603.23448/) (2026)
- [Trust Me, I Know This Function: Hijacking LLM Static Analysis using Bias](https://mlsanity.com/paper/2508.17361/) (2025)
- [Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale](https://mlsanity.com/paper/2511.08475/) (2025)
- [Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements](https://mlsanity.com/paper/2506.10330/) (2025)
- [Autonomous Legacy Web Application Upgrades Using a Multi-Agent System](https://mlsanity.com/paper/2501.19204/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [SEVRA-BENCH: Social Engineering of Vulnerabilities in Review Agents](https://mlsanity.com/paper/2606.13757/) (2026)
- [Grounded AI for Code Review: Resource-Efficient Large-Model Serving in Enterprise Pipelines](https://mlsanity.com/paper/2510.10290/) (2025)
- [From Specification to Service: Accelerating API-First Development Using Multi-Agent Systems](https://mlsanity.com/paper/2510.19274/) (2025)
- [More than a Judge: An Empirical Study of Agent-Human Interaction in Crowdsourced Testing Assessment](https://mlsanity.com/paper/2606.06301/) (2026)
- [From Human-Centric to Agentic Code Review: The Impact of Different Generations of Generative AI Technology on Review Quality](https://mlsanity.com/paper/2607.13196/) (2026)

## Abstract (arXiv)

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.