AI-powered Code Review with LLMs: Early Results
- URL: http://arxiv.org/abs/2404.18496v1
- Date: Mon, 29 Apr 2024 08:27:50 GMT
- Title: AI-powered Code Review with LLMs: Early Results
- Authors: Zeeshan Rasheed, Malik Abdul Sami, Muhammad Waseem, Kai-Kristian Kemell, Xiaofeng Wang, Anh Nguyen, Kari Systä, Pekka Abrahamsson,
- Abstract summary: We present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model.
Our proposed LLM-based AI agent model is trained on large code repositories.
It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code.
- Score: 10.37036924997437
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions.
We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types.
We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
arXiv Detail & Related papers (2024-07-08T17:27:17Z) - AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data [64.69872638349922]
We present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data.
We propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review.
arXiv Detail & Related papers (2024-05-29T16:57:33Z) - CodeCloak: A Method for Evaluating and Mitigating Code Leakage by LLM Code Assistants [23.462703429753706]
We propose two complementary methods to mitigate the risk of code leakage when using LLM-based code assistants.
The first is a technique for reconstructing a developer's original from code segments sent to the code assistant service.
The second is CodeCloak, a novel deep reinforcement learning agent that manipulates the prompts before sending them to the code assistant service.
arXiv Detail & Related papers (2024-04-13T19:30:58Z) - Automating Patch Set Generation from Code Review Comments Using Large Language Models [2.045040820541428]
We provide code contexts to five popular Large Language Models (LLMs)
We obtain the suggested code-changes (patch sets) derived from real-world code-review comments.
The performance of each model is meticulously assessed by comparing their generated patch sets against the historical data of human-generated patch-sets.
arXiv Detail & Related papers (2024-04-10T02:46:08Z) - Code Needs Comments: Enhancing Code LLMs with Comment Augmentation [91.52444946362547]
We introduce a novel data augmentation method that generates comments for existing code, coupled with a data filtering strategy that filters out code data poorly correlated with natural language.
We conducted experiments on three code-focused Large Language Models and observed consistent improvements in performance on two widely-used programming skill benchmarks.
arXiv Detail & Related papers (2024-02-20T13:56:38Z) - DolphCoder: Echo-Locating Code Large Language Models with Diverse and
Multi-Objective Instruction Tuning [36.78560777629329]
We introduce a diverse instruction model (DolphCoder) with self-evaluating for code generation.
It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability.
Our model achieves superior performance on the HumanEval and MBPP benchmarks.
arXiv Detail & Related papers (2024-02-14T12:34:58Z) - StepCoder: Improve Code Generation with Reinforcement Learning from
Compiler Feedback [58.20547418182074]
We introduce StepCoder, a novel framework for code generation, consisting of two main components.
CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks.
FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization.
Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
arXiv Detail & Related papers (2024-02-02T13:14:31Z) - Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs [69.99031792995348]
We introduce code prompting, a chain of prompts that transforms a natural language problem into code.
We find that code prompting exhibits a high-performance boost for multiple LLMs.
Our analysis of GPT 3.5 reveals that the code formatting of the input problem is essential for performance improvement.
arXiv Detail & Related papers (2024-01-18T15:32:24Z) - Neuron-level LLM Patching for Code Generation [32.178931149612644]
Large Language Models (LLMs) have found widespread adoption in software engineering, particularly in code generation tasks.
We propose a novel and effective model editing approach, textscMENT, to patch LLMs in coding tasks.
arXiv Detail & Related papers (2023-12-08T20:28:08Z) - LLM-Assisted Code Cleaning For Training Accurate Code Generators [53.087019724256606]
We investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system.
We build a novel data-cleaning pipeline that uses these principles to transform existing programs.
We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B improves the performance by up to 30% compared to fine-tuning on the original dataset.
arXiv Detail & Related papers (2023-11-25T02:45:50Z) - Test-Case-Driven Programming Understanding in Large Language Models for
Better Code Generation [15.166827643436346]
muFiX is a novel prompting technique to improve the code generation performance of large language models (LLMs)
It first exploits test case analysis to obtain specification understanding and enables a self-improvement process.
muFiX further fixes the specification understanding towards the direction reducing the gap between the provided understanding and the actual understanding.
arXiv Detail & Related papers (2023-09-28T02:58:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.