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20242026
most citedTesting Refactoring Engine via Historical Bug Report driven LLM

4 citations · 9 across the 15 of their papers we have counts for

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8 papers · 1 filter

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…