21 citations · 32 across the 16 of their papers we have counts for
5 papers · 1 filter
Fine-Tuning LLMs to Analyze Multiple Dimensions of Code Review: A Maximum Entropy Regulated Long Chain-of-Thought Approach
Yongda Yu, Guohao Shi, Xianwei Wu +8
Large Language Models (LLMs) have shown great potential in supporting automated code review due to their impressive capabilities in context understanding and reasoning. However, th…
Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation
Hongyu Kuang, Ning Zhang, Hui Gao +6
Commit messages are valuable resources for describing why code changes are committed to repositories in version control systems (e.g., Git). They effectively help developers unders…
SynthCoder: Anti-pattern identification and model training for FIM mode code completion
Dongjun Yu, Xiao Yan, Zhenrui Li +6
As a leading application of large language models (LLMs) in software engineering, Fill-in-the-Middle (FIM) mode code completion has drawn wide attention. Training such models requi…
Does AI Code Review Lead to Code Changes? A Case Study of GitHub Actions
Kexin Sun, Hongyu Kuang, Sebastian Baltes +6
AI-based code review tools automatically review and comment on pull requests to improve code quality. Despite their growing presence, little is known about their actual impact. We…
Distilling Desired Comments for Enhanced Code Review with Large Language Models
Yongda Yu, Lei Zhang, Guoping Rong +9
There has been a growing interest in using Large Language Models (LLMs) for code review thanks to their proven proficiency in code comprehension. The primary objective of most revi…