29 citations · 75 across the 12 of their papers we have counts for
13 papers
SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests
Yaoqi Guo, Yang Liu, Jie M. Zhang +3
Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories. B…
LLMs Are Not a Silver Bullet: A Case Study on Software Fairness
Xinyue Li, Sixuan Li, Ying Xiao +4
Fairness is a critical requirement for human-related, high-stakes software systems, motivating extensive research on bias mitigation. Prior work has largely focused on tabular data…
EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents
Yaoqi Guo, Ying Xiao, Jie M. Zhang +4
Software engineering (SE) agents powered by large language models are increasingly adopted in practice, yet they often incur substantial monetary cost. We introduce EET, an experie…
Software Fairness Dilemma: Is Bias Mitigation a Zero-Sum Game?
Zhenpeng Chen, Xinyue Li, Jie M. Zhang +5
Fairness is a critical requirement for Machine Learning (ML) software, driving the development of numerous bias mitigation methods. Previous research has identified a leveling-down…
FairMedQA: Benchmarking Bias in Large Language Models for Medical Question Answering
Ying Xiao, Jie Huang, Ruijuan He +6
Large language models (LLMs) are approaching expert-level performance in medical question answering (QA), demonstrating strong potential to improve public healthcare. However, unde…
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
Zhenpeng Chen, Xinyue Li, Jie M. Zhang +2
Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes. This paper intro…
Fairness Testing of Large Language Models in Role-Playing
Xinyue Li, Zhenpeng Chen, Jie M. Zhang +6
Large Language Models (LLMs) have become foundational in modern language-driven software applications, profoundly influencing daily life. A critical technique in leveraging their p…
Personality-Guided Code Generation Using Large Language Models
Yaoqi Guo, Zhenpeng Chen, Jie M. Zhang +2
Code generation, the automatic creation of source code from natural language descriptions, has garnered significant attention due to its potential to streamline software developmen…
LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs
Kaibo Liu, Zhenpeng Chen, Yiyang Liu +7
Detecting tricky bugs in plausible programs, those that pass existing test suites yet still contain bugs, remains a significant challenge in software testing. To address this probl…
Fairness Improvement with Multiple Protected Attributes: How Far Are We?
Zhenpeng Chen, Jie M. Zhang, Federica Sarro +1
Existing research mostly improves the fairness of Machine Learning (ML) software regarding a single protected attribute at a time, but this is unrealistic given that many users hav…
Fairness Testing: A Comprehensive Survey and Analysis of Trends
Zhenpeng Chen, Jie M. Zhang, Max Hort +2
Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedica…
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Max Hort, Zhenpeng Chen, Jie M. Zhang +2
This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias…
A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers
Zhenpeng Chen, Jie M. Zhang, Federica Sarro +1
Software bias is an increasingly important operational concern for software engineers. We present a large-scale, comprehensive empirical study of 17 representative bias mitigation…