89 citations · 93 across the 7 of their papers we have counts for
10 papers
Where Does the Performance Go? Understanding and Recovering Performance Regressions in C-to-Rust Translation
Zhiqiang Yuan, Chong Wang, Zhenhao Zhou +2
Rust is increasingly adopted as a memory-safe successor to C, and transpilers such as \crust{} can migrate entire C projects while preserving their behavior. Preserving behavior, h…
Benchmarking and Enhancing Design-Aware Code Review in the Linux Kernel
Zhenhao Zhou, Kai Yu, Ying Wang +5
Code review often requires reasoning beyond local correctness. In mature software systems, reviewers must recognize design trade-offs introduced by a proposed change, compare alter…
Does Pass Rate Tell the Whole Story? Evaluating Design Constraint Compliance in LLM-based Issue Resolution
Kai Yu, Zhenhao Zhou, Junhao Zeng +8
Repository-level issue resolution benchmarks have become a standard testbed for evaluating LLM-based agents, yet success is still predominantly measured by test pass rates. In prac…
Towards Iterative End-to-End Software Development: A Feature-Driven Multi-Agent Framework
Junwei Liu, Chen Xu, Chong Wang +5
Recent advances in large language model agents offer the promise of automating end-to-end software development from natural language requirements. However, existing approaches larg…
Code Digital Twin: Empowering LLMs with Tacit Knowledge for Complex Software Development
Xin Peng, Chong Wang
Recent advances in large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, raising expectations of revolutionary productivity gains. Howev…
Extracting Conceptual Knowledge to Locate Software Issues
Ying Wang, Wenjun Mao, Chong Wang +5
Issue localization, which identifies faulty code elements such as files or functions, is critical for effective bug fixing. While recent LLM-based and LLM-agent-based approaches im…
Project-Level C-to-Rust Translation via Pointer Knowledge Graphs
Zhiqiang Yuan, Wenjun Mao, Zhuo Chen +4
Translating C code into safe Rust is an effective way to ensure memory safety. Compared to rule-based approaches, which often produce largely unsafe Rust code, LLM-based methods ge…
Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults
Zhenhao Zhou, Zhuochen Huang, Yike He +5
The Linux kernel is a critical system, serving as the foundation for numerous systems. Bugs in the Linux kernel can cause serious consequences, affecting billions of users. Fault l…
Code Digital Twin: A Knowledge Infrastructure for AI-Assisted Complex Software Development
Xin Peng, Chong Wang
Recent advances in AI coding tools powered by large language models (LLMs) have shown strong capabilities in software engineering tasks, raising expectations of major productivity…
Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities
Xiangping Chen, Xing Hu, Yuan Huang +23
Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language p…