27 citations · 45 across the 32 of their papers we have counts for
31 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…
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.…
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…
BabelCoder: Agentic Code Translation with Specification Alignment
Fazle Rabbi, Soumit Kanti Saha, Tri Minh Triet Pham +2
As software systems evolve, developers increasingly work across multiple programming languages and often face the need to migrate code from one language to another. While automatic…
CFCEval: Evaluating Security Aspects in Code Generated by Large Language Models
Cheng Cheng, Jinqiu Yang
Code-focused Large Language Models (LLMs), such as CodeX and Star-Coder, have demonstrated remarkable capabilities in enhancing developer productivity through context-aware code ge…
Demystifying Errors in LLM Reasoning Traces: An Empirical Study of Code Execution Simulation
Mohammad Abdollahi, Khandaker Rifah Tasnia, Soumit Kanti Saha +3
Understanding a program's runtime reasoning behavior, meaning how intermediate states and control flows lead to final execution results, is essential for reliable code generation,…
Secure-Instruct: An Automated Pipeline for Synthesizing Instruction-Tuning Datasets Using LLMs for Secure Code Generation
Junjie Li, Fazle Rabbi, Bo Yang +2
Although Large Language Models (LLMs) show promising solutions to automated code generation, they often produce insecure code that threatens software security. Current approaches (…
ADPerf: Investigating and Testing Performance in Autonomous Driving Systems
Tri Minh-Triet Pham, Diego Elias Costa, Weiyi Shang +1
Obstacle detection is crucial to the operation of autonomous driving systems, which rely on multiple sensors, such as cameras and LiDARs, combined with code logic and deep learning…
RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
Feng Lin, Dong Jae Kim, Zhenhao Li +3
When using LLMs to address Non-Functional Requirements (NFRs), developers may behave differently (e.g., expressing the same NFR in different words). Robust LLMs should output consi…
A Multi-Language Perspective on the Robustness of LLM Code Generation
Fazle Rabbi, Zishuo Ding, Jinqiu Yang
Large language models have gained significant traction and popularity in recent times, extending their usage to code-generation tasks. While this field has garnered considerable at…
MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration
Yisen Xu, Feng Lin, Jinqiu Yang +3
Maintaining and scaling software systems relies heavily on effective code refactoring, yet this process remains labor-intensive, requiring developers to carefully analyze existing…
Specification-Driven Code Translation Powered by Large Language Models: How Far Are We?
Soumit Kanti Saha, Fazle Rabbi, Song Wang +1
Large Language Models (LLMs) are increasingly being applied across various domains, including code-related tasks such as code translation. Previous studies have explored using LLMs…
Bias Unveiled: Investigating Social Bias in LLM-Generated Code
Lin Ling, Fazle Rabbi, Song Wang +1
Large language models (LLMs) have significantly advanced the field of automated code generation. However, a notable research gap exists in evaluating social biases that may be pres…
Checker Bug Detection and Repair in Deep Learning Libraries
Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Jiho Shin +8
Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and…
Perception-Guided Fuzzing for Simulated Scenario-Based Testing of Autonomous Driving Systems
Tri Minh Triet Pham, Bo Yang, Jinqiu Yang
Autonomous Driving Systems (ADS) have made huge progress and started on-road testing or even commercializing trials. ADS are complex and difficult to test: they receive input data…
On the Robustness Evaluation of 3D Obstacle Detection Against Specifications in Autonomous Driving
Tri Minh Triet Pham, Bo Yang, Jinqiu Yang
Autonomous driving systems (ADSs) rely on real-time sensor data, such as cameras and LiDARs, for time-critical decisions using deep neural networks. The accuracy of these decisions…