7 papers
Stateful Testing for Deep Learning Compilers
Jiatao Ge, Ruixiang Qian, Yibo Hu +5
Modern deep learning compilers compile models lazily and cache artifacts across invocations. Retained compiler state can make a later invocation incorrect even when it is correct i…
Investigating Metamorphic Fuzz Oracle Enhancement via Large Language Models
Ruixiang Qian, Ding Yang, Zengxu Chen +4
Fuzz drivers are essential components of greybox fuzzing, as they encapsulate target interfaces, define test spaces, and largely determine fuzzing effectiveness. Existing fuzz driv…
Log-based, Business-aware REST API Testing
Ding Yang, Ruixiang Qian, Zhao Wei +2
REST APIs enable collaboration among microservices. A single fault in a REST API can bring down the entire microservice system and cause significant financial losses, underscoring…
Peeling Off the Cocoon: Unveiling Suppressed Golden Seeds for Mutational Greybox Fuzzing
Ruixiang Qian, Chunrong Fang, Zengxu Chen +2
PoCo is a technique that aims to enhance modern coverage-based seed selection (CSS) techniques (such as afl-cmin) by gradually removing obstacle conditional statements and conducti…
Deep Learning Framework Testing via Model Mutation: How Far Are We?
Yanzhou Mu, Rong Wang, Juan Zhai +7
Deep Learning (DL) frameworks are a fundamental component of DL development. Therefore, the detection of DL framework defects is important and challenging. As one of the most widel…
Improving Retrieval-Augmented Deep Assertion Generation via Joint Training
Quanjun Zhang, Chunrong Fang, Yi Zheng +7
Unit testing attempts to validate the correctness of basic units of the software system under test and has a crucial role in software development and testing. Very recent work prop…
Investigating Coverage Guided Fuzzing with Mutation Testing
Ruixiang Qian, Quanjun Zhang, Chunrong Fang +1
Coverage guided fuzzing (CGF) is an effective testing technique which has detected hundreds of thousands of bugs from various software applications. It focuses on maximizing code c…