64 citations · 173 across the 10 of their papers we have counts for
11 papers
PurpCode: Reasoning for Safer Code Generation
Jiawei Liu, Nirav Diwan, Zhe Wang +11
We introduce PurpCode, the first post-training recipe for training safe code reasoning models towards generating secure code and defending against malicious cyberactivities. PurpCo…
Agentless: Demystifying LLM-based Software Engineering Agents
Chunqiu Steven Xia, Yinlin Deng, Soren Dunn +1
Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test gen…
NExT: Teaching Large Language Models to Reason about Code Execution
Ansong Ni, Miltiadis Allamanis, Arman Cohan +4
A fundamental skill among human developers is the ability to understand and reason about program execution. As an example, a programmer can mentally simulate code execution in natu…
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM
Chunqiu Steven Xia, Yinlin Deng, Lingming Zhang
LLMs have become the go-to choice for code generation tasks, with an exponential increase in the training, development, and usage of LLMs specifically for code generation. To evalu…
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…
Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation
Jiawei Liu, Yuxiang Wei, Sen Yang +2
In the past decade, Deep Learning (DL) systems have been widely deployed in various domains to facilitate our daily life. Meanwhile, it is extremely challenging to ensure the corre…