7 papers
Improving Deep Learning Library Testing with Machine Learning
Facundo Molina, M M Abid Naziri, Feiran Qin +2
Deep Learning (DL) libraries like TensorFlow and Pytorch simplify machine learning (ML) model development but are prone to bugs due to their complex design. Bug-finding techniques…
Testing Deep Learning Libraries via Neurosymbolic Constraint Learning
M M Abid Naziri, Shinhae Kim, Feiran Qin +2
Deep Learning (DL) libraries (e.g., PyTorch) are popular in AI development. These libraries are complex and contain bugs. Researchers have proposed various bug-finding techniques f…
Misbehavior Forecasting for Focused Autonomous Driving Systems Testing
M M Abid Naziri, Stefano Carlo Lambertenghi, Andrea Stocco +1
Simulation-based testing is the standard practice for assessing the reliability of self-driving cars' software before deployment. Existing bug-finding techniques are either unrelia…
Configuration Defects in Kubernetes
Yue Zhang, Uchswas Paul, Marcelo d'Amorim +1
Kubernetes is a tool that facilitates rapid deployment of software. Unfortunately, configuring Kubernetes is prone to errors. Configuration defects are not uncommon and can result…
XMutant: XAI-based Fuzzing for Deep Learning Systems
Xingcheng Chen, Matteo Biagiola, Vincenzo Riccio +2
Semantic-based test generators are widely used to produce failure-inducing inputs for Deep Learning (DL) systems. They typically generate challenging test inputs by applying random…
Bug Histories as Sources of Compiler Fuzzing Mutators
Lingjun Liu, Feiran Qin, Owolabi Legunsen +1
Bugs in compilers, which are critical infrastructure today, can have outsized negative impacts. Mutational fuzzers aid compiler bug detection by systematically mutating compiler in…