375 citations · 1.5k across the 26 of their papers we have counts for
26 papers
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…
NeuRI: Diversifying DNN Generation via Inductive Rule Inference
Jiawei Liu, Jinjun Peng, Yuyao Wang +1
Deep Learning (DL) is prevalently used in various industries to improve decision-making and automate processes, driven by the ever-evolving DL libraries and compilers. The correctn…
Conversational Automated Program Repair
Chunqiu Steven Xia, Lingming Zhang
Automated Program Repair (APR) can help developers automatically generate patches for bugs. Due to the impressive performance obtained using Large Pre-Trained Language Models (LLMs…
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…
Practical Program Repair in the Era of Large Pre-trained Language Models
Chunqiu Steven Xia, Yuxiang Wei, Lingming Zhang
Automated Program Repair (APR) aims to help developers automatically patch software bugs. However, current state-of-the-art traditional and learning-based APR techniques face the p…
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…
NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
Jiawei Liu, Jinkun Lin, Fabian Ruffy +4
Deep-learning (DL) compilers such as TVM and TensorRT are increasingly being used to optimize deep neural network (DNN) models to meet performance, resource utilization and other r…
Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-shot Learning
Chunqiu Steven Xia, Lingming Zhang
Due to the promising future of Automated Program Repair (APR), researchers have proposed various APR techniques, including heuristic-based, template-based, and constraint-based tec…
Attention: Not Just Another Dataset for Patch-Correctness Checking
Jun Yang, Yuehan Wang, Yiling Lou +2
Automated Program Repair (APR) techniques have drawn wide attention from both academia and industry. Meanwhile, one main limitation with the current state-of-the-art APR tools is t…
Program Repair: Automated vs. Manual
Quanjun Zhang, Yuan Zhao, Weisong Sun +3
Various automated program repair (APR) techniques have been proposed to fix bugs automatically in the last decade. Although recent researches have made significant progress on the…
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…
Self-Boosted Automated Program Repair
Samuel Benton, Mengshi Zhang, Xia Li +1
Program repair is an integral part of every software system's life-cycle but can be extremely challenging. To date, researchers have proposed various automated program repair (APR)…
Fast and Precise On-the-fly Patch Validation for All
Lingchao Chen, Lingming Zhang
Generate-and-validate (G&V) automated program repair (APR) techniques have been extensively studied during the past decade. Meanwhile, such techniques can be extremely time-consumi…
Can Automated Program Repair Refine Fault Localization?
Yiling Lou, Ali Ghanbari, Xia Li +3
Software bugs are prevalent in modern software systems and notoriously hard to debug manually. Therefore, a large body of research efforts have been dedicated to automated software…
Mutation Testing for Ethereum Smart Contract
Haoran Wu, Xingya Wang, Jiehui Xu +3
Smart contract is a special program that manages digital assets on blockchain. It is difficult to recover the loss if users make transactions through buggy smart contracts, which c…
Characterizing and Detecting CUDA Program Bugs
Mingyuan Wu, Husheng Zhou, Lingming Zhang +2
While CUDA has become a major parallel computing platform and programming model for general-purpose GPU computing, CUDA-induced bug patterns have not yet been well explored. In thi…
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems
Husheng Zhou, Wei Li, Yuankun Zhu +4
Deep Neural Networks (DNNs) have been widely applied in many autonomous systems such as autonomous driving. Recently, DNN testing has been intensively studied to automatically gene…
Practical Program Repair via Bytecode Mutation
Ali Ghanbari, Lingming Zhang
Software debugging is tedious, time-consuming, and even error-prone by itself. So, various automated debugging techniques have been proposed in the literature to facilitate the deb…