5 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…
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…
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…