3 citations · 7 across the 13 of their papers we have counts for
14 papers
Executable but Unfaithful: Mock Object, Stub, and Assertion Generation by LLMs and Coding Agents
Hao Ning, Jiahui Dong, Zhen Yang +7
Mocking is a foundational practice in unit testing, enabling developers to isolate the code under test by replacing real dependencies with simulated alternatives. Automating the ge…
CausalRepair: Bridging the Causality Gap in Large Language Model-Based Automated Program Repair via Dual-Slicing
Linhao Wu, Yizhou Chen, Zhen Yang +2
Automated Program Repair (APR) has recently benefited from Large Language Models (LLMs), yet their effectiveness heavily depends on repair context. Existing LLM-based APR methods s…
DebugRepair: Enhancing LLM-Based Automated Program Repair via Self-Directed Debugging
Linhao Wu, Yifei Pei, Zhen Yang +9
Automated Program Repair (APR) has benefited from the code understanding and generation capabilities of Large Language Models (LLMs). Existing feedback-based APR methods iterativel…
SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
Zhengyang Shan, Xu Qian, Jiayun Xin +5
Software vulnerabilities are a primary threat to modern infrastructure. While static analysis and Graph Neural Networks have long served as the foundation for vulnerability detecti…
R2ComSync: Improving Code-Comment Synchronization with In-Context Learning and Reranking
Zhen Yang, Hongyi Lin, Xiao Yu +5
Code-Comment Synchronization (CCS) aims to synchronize the comments with code changes in an automated fashion, thereby significantly reducing the workload of developers during soft…
Towards Speeding up Program Repair with Non-Autoregressive Model
Zhenyu Yang, Yue Pan, Zhen Yang +1
Enlightened by the success of machine learning techniques in various application areas, recent years have witnessed a surge of research efforts on automatic program repair (APR) us…
Contamination Means Overestimation? A Fine-Grained Empirical Study in Code Intelligence
Zhen Yang, Hongyi Lin, Yifan He +7
In recent years, code intelligence has gained increasing importance in the field of automated software engineering. Meanwhile, the widespread adoption of Pretrained Language Models…
Parameter-Efficient Fine-Tuning with Attributed Patch Semantic Graph for Automated Patch Correctness Assessment
Zhenyu Yang, Jingwen Wu, Zhen Yang +1
Automated program repair (APR) aims to automatically repair program errors without human intervention, and recent years have witnessed a growing interest on this research topic. Wh…
Compiler Optimization Testing Based on Optimization-Guided Equivalence Transformations
Jingwen Wu, Jiajing Zheng, Zhenyu Yang +1
Compiler optimization techniques are inherently complex, and rigorous testing of compiler optimization implementation is critical. Recent years have witnessed the emergence of test…
ClassEval-T: Evaluating Large Language Models in Class-Level Code Translation
Pengyu Xue, Linhao Wu, Zhen Yang +9
In recent years, Large Language Models (LLMs) have dramatically advanced the performance of automated code translation, making their computational accuracy score reach up to over 8…
Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing
Pengyu Xue, Linhao Wu, Zhen Yang +8
In recent years, Large language model-powered Automated Program Repair (LAPR) techniques have achieved state-of-the-art bug-fixing performance and have been pervasively applied and…
NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repair
Zhenyu Yang, Zhen Yang, Zhongxing Yu
With the advancement of deep learning techniques, the performance of Automatic Program Repair(APR) techniques has reached a new level. Previous deep learning-based APR techniques e…
Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
Zhen Yang, Fang Liu, Zhongxing Yu +7
Code translation tools (transpilers) are developed for automatic source-to-source translation. Although learning-based transpilers have shown impressive enhancement against rule-ba…
Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond
Pengyu Xue, Linhao Wu, Zhongxing Yu +5
Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs, which facilitate collaboration among developers and play a criti…