163 citations · 302 across the 32 of their papers we have counts for
6 papers · 1 filter
CCBERT: Self-Supervised Code Change Representation Learning
Xin Zhou, Bowen Xu, DongGyun Han +3
Numerous code changes are made by developers in their daily work, and a superior representation of code changes is desired for effective code change analysis. Recently, Hoang et al…
Greening Large Language Models of Code
Jieke Shi, Zhou Yang, Hong Jin Kang +3
Large language models of code have shown remarkable effectiveness across various software engineering tasks. Despite the availability of many cloud services built upon these powerf…
Generation-based Code Review Automation: How Far Are We?
Xin Zhou, Kisub Kim, Bowen Xu +3
Code review is an effective software quality assurance activity; however, it is labor-intensive and time-consuming. Thus, a number of generation-based automatic code review (ACR) a…
Representation Learning for Stack Overflow Posts: How Far are We?
Junda He, Zhou Xin, Bowen Xu +6
The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzi…
PatchZero: Zero-Shot Automatic Patch Correctness Assessment
Xin Zhou, Bowen Xu, Kisub Kim +5
Automated Program Repair (APR) techniques have shown more and more promising results in fixing real-world bugs. Despite the effectiveness, APR techniques still face an overfitting…
Stealthy Backdoor Attack for Code Models
Zhou Yang, Bowen Xu, Jie M. Zhang +4
Code models, such as CodeBERT and CodeT5, offer general-purpose representations of code and play a vital role in supporting downstream automated software engineering tasks. Most re…