4 citations · 9 across the 15 of their papers we have counts for
15 papers
Understanding the Energy Impact of Software Refactoring: A Workload-Aware Study of Controlled Examples and Real-World Commits
Haibo Wang, Heng Li, Shin Hwei Tan
Refactoring improves software maintainability while preserving functional behavior, yet behavior preservation does not imply energy neutrality. Existing studies primarily examine i…
Ethics Testing: Proactive Identification of Generative AI System Harms
Shin Hwei Tan, Haibo Wang, Heng Li
Generative Artificial Intelligence (GAI) systems that can automatically generate content in the form of source code or other contents (e.g., images) has seen increasing popularity…
Bidirectional Code Reuse in Software Redesign: An Action Research Study of Static Analyzers
Xiaowen Zhang, Huaien Zhang, Shin Hwei Tan
Software redesign preserves functionality while improving quality attributes, but manual reuse of code and tests is costly and error-prone, especially in cross-repository redesigns…
COBOL-Coder: Domain-Adapted Large Language Models for COBOL Code Generation and Translation
Anh T. V. Dau, Shin Hwei Tan, Jinqiu Yang +2
COBOL remains a critical language for mainframe systems, yet existing large language models (LLMs) struggle to generate and translate COBOL code correctly. This paper reports our e…
COBOLAssist: Analyzing and Fixing Compilation Errors for LLM-Powered COBOL Code Generation
Anh T. V. Dau, Shin Hwei Tan, Jinqiu Yang +2
Legacy programming languages such as COBOL (Common Business-Oriented Language) remain critical in business computing. However, maintaining legacy COBOL systems is increasingly chal…
Think Before You Code: Dual Reasoning for the NLSafety-Utility Trade-Off in LLM Code Generation
Honghao Tan, Haibo Wang, Shin Hwei Tan
Large language models (LLMs) for code generation are typically evaluated on functional correctness alone, overlooking whether generated code propagates harmful content embedded in…
Understanding Bugs in Modern Agentic Frameworks: A Study of Symptoms, Root Causes, and Triggering Conditions
Xiaowen Zhang, Hannuo Zhang, Shin Hwei Tan
Modern agentic frameworks such as CrewAI and AutoGen have evolved into complex, autonomous multi-agent systems, introducing reliability challenges that go beyond earlier pipeline-b…
Probe to Generate: Program Variant-Guided Test Augmentation for Repository-Level Repair Benchmarks
Chenglin Li, Yisen Xu, Zehao Wang +3
Test-based benchmarks such as SWE-bench have become a standard basis for evaluating automated issue resolution agents, deeming a patch correct if it passes a provided regression te…
BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
Yinghang Ma, Jiho Shin, Leuson Da Silva +5
Recent advances in large language models (LLMs) have accelerated the development of AI-driven automated program repair (APR) solutions. However, these solutions are typically evalu…
What Makes Code Generation Ethically Sourced?
Zhuolin Xu, Chenglin Li, Qiushi Li +1
Several code generation models have been proposed to help reduce time and effort in solving software-related tasks. To ensure responsible AI, there are growing interests over vario…
Automated Harmfulness Testing for Code Large Language Models
Honghao Tan, Haibo Wang, Diany Pressato +2
Generative AI systems powered by Large Language Models (LLMs) usually use content moderation to prevent harmful content spread. To evaluate the robustness of content moderation, se…
Testing Refactoring Engine via Historical Bug Report driven LLM
Haibo Wang, Zhuolin Xu, Shin Hwei Tan
Refactoring is the process of restructuring existing code without changing its external behavior while improving its internal structure. Refactoring engines are integral components…
An Empirical Study of Refactoring Engine Bugs
Haibo Wang, Zhuolin Xu, Huaien Zhang +2
Refactoring is a critical process in software development, aiming at improving the internal structure of code while preserving its external behavior. Refactoring engines are integr…
An Empirical Study of False Negatives and Positives of Static Code Analyzers From the Perspective of Historical Issues
Han Cui, Menglei Xie, Ting Su +2
Static code analyzers are widely used to help find program flaws. However, in practice the effectiveness and usability of such analyzers is affected by the problems of false negati…
Aligning the Objective of LLM-based Program Repair
Junjielong Xu, Ying Fu, Shin Hwei Tan +1
Large language models (LLMs) have achieved decent results on automated program repair (APR). However, the next token prediction training objective of decoder-only LLMs (e.g., GPT-4…