A GPT-based Code Review System for Programming Language Learning
- URL: http://arxiv.org/abs/2407.04722v1
- Date: Fri, 21 Jun 2024 12:16:01 GMT
- Title: A GPT-based Code Review System for Programming Language Learning
- Authors: Lee Dong-Kyu,
- Abstract summary: This research proposes a system that employs GPT-4 to offer learner-friendly code reviews and minimize the risk of AI-assist cheating.
The improved system underwent evaluation by software education experts based on four criteria: strict code correctness checks, response time, lower API call costs, and the quality of code reviews.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The increasing demand for programming language education and growing class sizes require immediate and personalized feedback. However, traditional code review methods have limitations in providing this level of feedback. As the capabilities of Large Language Models (LLMs) like GPT for generating accurate solutions and timely code reviews are verified, this research proposes a system that employs GPT-4 to offer learner-friendly code reviews and minimize the risk of AI-assist cheating. To provide learner-friendly code reviews, a dataset was collected from an online judge system, and this dataset was utilized to develop and enhance the system's prompts. In addition, to minimize AI-assist cheating, the system flow was designed to provide code reviews only for code submitted by a learner, and a feature that highlights code lines to fix was added. After the initial system was deployed on the web, software education experts conducted usability test. Based on the results, improvement strategies were developed to improve code review and code correctness check module, thereby enhancing the system. The improved system underwent evaluation by software education experts based on four criteria: strict code correctness checks, response time, lower API call costs, and the quality of code reviews. The results demonstrated a performance to accurately identify error types, shorten response times, lower API call costs, and maintain high-quality code reviews without major issues. Feedback from participants affirmed the tool's suitability for teaching programming to primary and secondary school students. Given these benefits, the system is anticipated to be a efficient learning tool in programming language learning for educational settings.
Related papers
- AI-Assisted Assessment of Coding Practices in Modern Code Review [11.803776132972029]
AutoCommenter is an end-to-end system for learning and enforcing coding best practices.
This paper reports on the development, deployment, and evaluation of AutoCommenter.
arXiv Detail & Related papers (2024-05-22T11:57:18Z) - AI-powered Code Review with LLMs: Early Results [10.37036924997437]
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.
arXiv Detail & Related papers (2024-04-29T08:27:50Z) - Improving the Validity of Automatically Generated Feedback via
Reinforcement Learning [50.067342343957876]
We propose a framework for feedback generation that optimize both correctness and alignment using reinforcement learning (RL)
Specifically, we use GPT-4's annotations to create preferences over feedback pairs in an augmented dataset for training via direct preference optimization (DPO)
arXiv Detail & Related papers (2024-03-02T20:25:50Z) - Improving Automated Code Reviews: Learning from Experience [12.573740138977065]
This study investigates whether higher-quality reviews can be generated from automated code review models.
We find that experience-aware oversampling can increase the correctness, level of information, and meaningfulness of reviews.
arXiv Detail & Related papers (2024-02-06T07:48:22Z) - 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) - ReviewRanker: A Semi-Supervised Learning Based Approach for Code Review
Quality Estimation [0.6895577977557867]
Inspection of review process effectiveness and continuous improvement can boost development productivity.
We propose a semi-supervised learning based system ReviewRanker which is aimed at assigning each code review a confidence score.
Our proposed method is trained based on simple and and well defined labels provided by developers.
arXiv Detail & Related papers (2023-07-08T15:37:48Z) - The Wisdom of Hindsight Makes Language Models Better Instruction
Followers [84.9120606803906]
Reinforcement learning has seen wide success in finetuning large language models to better align with instructions via human feedback.
In this paper, we consider an alternative approach: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner.
We propose Hindsight Instruction Relabeling (HIR), a novel algorithm for aligning language models with instructions.
arXiv Detail & Related papers (2023-02-10T12:16:38Z) - CodeRL: Mastering Code Generation through Pretrained Models and Deep
Reinforcement Learning [92.36705236706678]
"CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning.
During inference, we introduce a new generation procedure with a critical sampling strategy.
For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
arXiv Detail & Related papers (2022-07-05T02:42:15Z) - CodeReviewer: Pre-Training for Automating Code Review Activities [36.40557768557425]
This research focuses on utilizing pre-training techniques for the tasks in the code review scenario.
We collect a large-scale dataset of real world code changes and code reviews from open-source projects in nine of the most popular programming languages.
To better understand code diffs and reviews, we propose CodeReviewer, a pre-trained model that utilizes four pre-training tasks tailored specifically for the code review senario.
arXiv Detail & Related papers (2022-03-17T05:40:13Z) - ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
In this paper, we frame the problem of providing feedback as few-shot classification.
A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors.
Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
arXiv Detail & Related papers (2021-07-23T22:41:28Z) - Measuring Coding Challenge Competence With APPS [54.22600767666257]
We introduce APPS, a benchmark for code generation.
Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges.
Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems.
arXiv Detail & Related papers (2021-05-20T17:58:42Z)
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.