12 citations · 50 across the 18 of their papers we have counts for
8 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…
The Devil is in the Tails: How Long-Tailed Code Distributions Impact Large Language Models
Xin Zhou, Kisub Kim, Bowen Xu +3
Learning-based techniques, especially advanced Large Language Models (LLMs) for code, have gained considerable popularity in various software engineering (SE) tasks. However, most…
How are We Detecting Inconsistent Method Names? An Empirical Study from Code Review Perspective
Kisub Kim, Xin Zhou, Dongsun Kim +6
Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication…
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
Martin Weyssow, Xin Zhou, Kisub Kim +2
Large language models (LLMs) demonstrate impressive capabilities to generate accurate code snippets given natural language intents in a zero-shot manner, i.e., without the need for…
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
Martin Weyssow, Xin Zhou, Kisub Kim +2
Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general know…
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…
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…
Prioritizing Speech Test Cases
Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi +4
With the wide adoption of automated speech recognition (ASR) systems, it is increasingly important to test and improve ASR systems. However, collecting and executing speech test ca…