27 citations · 45 across the 32 of their papers we have counts for
13 papers · 1 filter
RETRACE: From Entangled Repair Histories to Reusable Experience for CI Repair
Rabeya Khatun Muna, Muhammad Ahasanuzzaman, Nakhla Rafi +3
Large language model (LLM) agents increasingly reuse prior experience, but most approaches assume that problems and solutions are already aligned. Software histories rarely provide…
Checked-In Secret Detection: Strings Are All You Need
Zhengdong Huang, Kevin Li, Jinqiu Yang +2
Hardcoded secrets in source code pose critical security vulnerabilities which can be easily exploited by malicious adversaries. Existing regex-based detection approaches suffer fro…
Preventing Premature Commitment in Coding Agents with an Evidence-Conditioned Execution Layer
Yisen Xu, Chenglin Li, Zehao Wang +2
LLM-based coding agents often edit source code or submit patches before examining enough repository evidence to justify the change, a failure pattern we call premature commitment.…
IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
Ankur Singh, Jinqiu Yang, Tse-Hsun Chen
AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files…
HEJ-Robust: A Robustness Benchmark for LLM-Based Automated Program Repair
Fazle Rabbi, Jinqiu Yang
Recent Large Language Models (LLMs) have shown strong performance on automated program repair across standard benchmarks. However, these benchmarks evaluate models on a single cano…
Beyond Translation Accuracy: Addressing False Failures in LLM-Based Code Translation
Fazle Rabbi, Soumit Kanti Saha, Jinqiu Yang
Large Language Models (LLMs) have achieved remarkable success in automated code translation. While prior work has focused on improving translation accuracy through advanced prompti…
Social Bias in LLM-Generated Code: Benchmark and Mitigation
Fazle Rabbi, Lin Ling, Song Wang +1
Large Language Models (LLMs) are increasingly deployed to generate code for human-centered applications where demographic fairness is critical. However, existing evaluations focus…
ABTest: Behavior-Driven Testing for AI Coding Agents
Wuyang Dai, Moses Openja, Hung Viet Pham +3
AI coding agents are increasingly integrated into real-world software development workflows, yet their robustness under diverse and adversarial scenarios remains poorly understood.…
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…
LLM4Log: A Systematic Review of Large Language Model-based Log Analysis
Zeyang Ma, Jinqiu Yang, Tse-Hsun Chen
Software systems generate massive, evolving, semi-structured logs that are central to reliability engineering and AIOps, yet difficult to analyze at scale under drift and limited l…
Engineering Pitfalls in AI Coding Tools: An Empirical Study of Bugs in Claude Code, Codex, and Gemini CLI
Ruixin Zhang, Wuyang Dai, Hung Viet Pham +3
The rapid integration of Large Language Models (LLMs) into software development workflows has given rise to a new class of AI-assisted coding tools, such as Claude-Code, Codex, and…
SWE-Refactor: A Repository-Level Benchmark for Real-World LLM-Based Code Refactoring
Yisen Xu, Jinqiu Yang, Tse-Hsun +1
Large Language Models (LLMs) have recently attracted wide interest for tackling software engineering tasks. In contrast to code generation, refactoring demands precise, semantics-p…