6 papers
We Must Have Missed This Comment: Detecting and Repairing Stale Function References in Linux Kernel Comments
Kexin Sun, Yunbo Lyu, Xutong Ma +6
As the Linux kernel evolves, code comments may become outdated, as the functions they reference can be refactored or removed independently without corresponding updates to the comm…
Improving LLM-Based Go Code Review through Issue-List Generation and Context Augmentation
Kexin Sun, Yucong Guan, Jiaqi Sun +6
LLMs have shown strong potential for automating code review, yet their practical utility depends heavily on the design of generation and context strategies. In this paper, we inves…
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…
AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context
Lei Zhang, Yongda Yu, Minghui Yu +11
High-quality evaluation benchmarks are pivotal for deploying Large Language Models (LLMs) in Automated Code Review (ACR). However, existing benchmarks suffer from two critical limi…
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…
AUCAD: Automated Construction of Alignment Dataset from Log-Related Issues for Enhancing LLM-based Log Generation
Hao Zhang, Dongjun Yu, Lei Zhang +6
Log statements have become an integral part of modern software systems. Prior research efforts have focused on supporting the decisions of placing log statements, such as where/wha…