21 citations · 21 across the 6 of their papers we have counts for
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As the Linux kernel evolves, code comments may become outdated, as the functions they reference can be refactored or removed independently without corresponding updates to the comm…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…
AVIATE: Exploiting Translation Variants of Artifacts to Improve IR-based Traceability Recovery in Bilingual Software Projects
Kexin Sun, Yiding Ren, Hongyu Kuang +5
Traceability plays a vital role in facilitating various software development activities by establishing the traces between different types of artifacts (e.g., issues and commits in…
Using Consensual Biterms from Text Structures of Requirements and Code to Improve IR-Based Traceability Recovery
Hui Gao, Hongyu Kuang, Kexin Sun +6
Traceability approves trace links among software artifacts based on whether two artifacts are related by system functionalities. The traces are valuable for software development, b…
Exploiting the Unique Expression for Improved Sentiment Analysis in Software Engineering Text
Kexin Sun, Hui Gao, Hongyu Kuang +4
Sentiment analysis on software engineering (SE) texts has been widely used in the SE research, such as evaluating app reviews or analyzing developers sentiments in commit messages.…