121 citations · 152 across the 18 of their papers we have counts for
19 papers
Towards Automatically Pruning Logging Code with Coding Agents: How Far Are We?
He Yang Yuan, Haonan Zhang, Xin Wang +4
Logging code supports debugging, monitoring, and software maintenance, but excessive logging can add noise, impose runtime overhead, and obscure diagnostic information. While prior…
A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
Xin Zhou, Chun Yong Chong, Kisub Kim +11
Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs. Yet existing repository-level…
iCodeReviewer: Improving Secure Code Review with Mixture of Prompts
Yun Peng, Kisub Kim, Linghan Meng +1
Code review is an essential process to ensure the quality of software that identifies potential software issues at an early stage of software development. Among all software issues…
Curiosity-Driven Testing for Sequential Decision-Making Process
Junda He, Zhou Yang, Jieke Shi +5
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent adv…
An LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks
Xin Zhou, Kisub Kim, Ting Zhang +6
Large Language Models (LLMs) and other automated techniques have been increasingly used to support software developers by generating software artifacts such as code snippets, patch…
Multi-LLM Collaboration + Data-Centric Innovation = 2x Better Vulnerability Repair
Xin Zhou, Kisub Kim, Bowen Xu +2
The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fi…
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…
Are We Ready to Embrace Generative AI for Software Q&A?
Bowen Xu, Thanh-Dat Nguyen, Thanh Le-Cong +8
Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Ov…
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…
APISENS- Sentiment Scoring Tool for APIs with Crowd-Knowledge
Kisub Kim, Ferdian Thung, Ting Zhang +4
Utilizing pre-existing software artifacts, such as libraries and Application Programming Interfaces (APIs), is crucial for software development efficiency. However, the abundance o…
APIHarvest: Harvesting API Information from Various Online Sources
Ferdian Thung, Kisub Kim, Ting Zhang +4
Using APIs to develop software applications is the norm. APIs help developers to build applications faster as they do not need to reinvent the wheel. It is therefore important for…
PICASO: Enhancing API Recommendations with Relevant Stack Overflow Posts
Ivana Clairine Irsan, Ting Zhang, Ferdian Thung +2
While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowaday…
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…
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Tiezhu Sun, Kevin Allix, Kisub Kim +5
The automation of a large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). Central to applying ML to software artifacts (like source or ex…
On the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs
Kui Liu, Shangwen Wang, Anil Koyuncu +7
Test-based automated program repair has been a prolific field of research in software engineering in the last decade. Many approaches have indeed been proposed, which leverage test…