64 citations · 193 across the 11 of their papers we have counts for
11 papers
Code as Agent Harness
Xuying Ning, Katherine Tieu, Dongqi Fu +39
Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineerin…
AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection
Zijie Zhao, Chenyuan Yang, Weidong Wang +3
While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation, limiting the practicality of a…
KNighter: Transforming Static Analysis with LLM-Synthesized Checkers
Chenyuan Yang, Zijie Zhao, Zichen Xie +2
Static analysis is a powerful technique for bug detection in critical systems like operating system kernels. However, designing and implementing static analyzers is challenging, ti…
TESTEVAL: Benchmarking Large Language Models for Test Case Generation
Wenhan Wang, Chenyuan Yang, Zhijie Wang +6
Testing plays a crucial role in the software development cycle, enabling the detection of bugs, vulnerabilities, and other undesirable behaviors. To perform software testing, teste…
KernelGPT: Enhanced Kernel Fuzzing via Large Language Models
Chenyuan Yang, Zijie Zhao, Lingming Zhang
Bugs in operating system kernels can affect billions of devices and users all over the world. As a result, a large body of research has been focused on kernel fuzzing, i.e., automa…
WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models
Chenyuan Yang, Yinlin Deng, Runyu Lu +4
Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences. Fuzzing has been studied to uncover compiler defects. However, co…
Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT
Yinlin Deng, Chunqiu Steven Xia, Chenyuan Yang +3
Deep Learning (DL) library bugs affect downstream DL applications, emphasizing the need for reliable systems. Generating valid input programs for fuzzing DL libraries is challengin…
Fuzzing Automatic Differentiation in Deep-Learning Libraries
Chenyuan Yang, Yinlin Deng, Jiayi Yao +3
Deep learning (DL) has attracted wide attention and has been widely deployed in recent years. As a result, more and more research efforts have been dedicated to testing DL librarie…
Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models
Yinlin Deng, Chunqiu Steven Xia, Haoran Peng +2
Detecting bugs in Deep Learning (DL) libraries (e.g., TensorFlow/PyTorch) is critical for almost all downstream DL systems in ensuring effectiveness/safety for end users. Meanwhile…
Fuzzing Deep-Learning Libraries via Automated Relational API Inference
Yinlin Deng, Chenyuan Yang, Anjiang Wei +1
A growing body of research has been dedicated to DL model testing. However, there is still limited work on testing DL libraries, which serve as the foundations for building, traini…
Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source
Anjiang Wei, Yinlin Deng, Chenyuan Yang +1
Deep learning (DL) systems can make our life much easier, and thus are gaining more and more attention from both academia and industry. Meanwhile, bugs in DL systems can be disastr…