24 citations · 35 across the 4 of their papers we have counts for
4 papers
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…
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 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…