186 citations · 693 across the 16 of their papers we have counts for
19 papers · 1 filter
Fuzz4All: Universal Fuzzing with Large Language Models
Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian +2
Fuzzing has achieved tremendous success in discovering bugs and vulnerabilities in various software systems. Systems under test (SUTs) that take in programming or formal language a…
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang +1
Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code. Programming benchmarks, with cu…
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…
Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT
Chunqiu Steven Xia, Lingming Zhang
Automated Program Repair (APR) aims to automatically generate patches for buggy programs. Recent APR work has been focused on leveraging modern Large Language Models (LLMs) to dire…
Revisiting the Plastic Surgery Hypothesis via Large Language Models
Chunqiu Steven Xia, Yifeng Ding, Lingming Zhang
Automated Program Repair (APR) aspires to automatically generate patches for an input buggy program. Traditional APR tools typically focus on specific bug types and fixes through t…
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…