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…
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…
Evaluating the Effectiveness of Coverage-Guided Fuzzing for Testing Deep Learning Library APIs
Feiran Qin, M. M. Abid Naziri, Hengyu Ai +2
Deep Learning (DL) libraries such as PyTorch provide the core components to build major AI-enabled applications. Finding bugs in these libraries is important and challenging. Prior…