StarV: A Qualitative and Quantitative Verification Tool for Learning-Enabled Systems

Hoang-Dung Tran, Sung Woo Choi, Yuntao Li, Qing Liu, Hideki Okamoto, Bardh Hoxha, Georgios Fainekos

Abstract: This paper presents StarV, a new tool for verifying deep neural networks (DNNs) and learning-enabled Cyber-Physical Systems (Le-CPS) using the well-known star reachability. Distinguished from existing star-based verification tools such as NNV and NNENUM and others, StarV not only offers qualitative verification techniques using Star and ImageStar reachability analysis but is also the first tool to propose using ProbStar reachability for quantitative verification of DNNs with piecewise linear activation functions and Le-CPS. Notably, it introduces a novel ProbStar Temporal Logic formalism and associated algorithms, enabling the quantitative verification of DNNs and Le-CPS’s temporal behaviors. Additionally, StarV presents a novel SparseImageStar set representation and associated reachability algorithm that allows users to verify deep convolutional neural networks and semantic segmentation networks with more memory efficiency. StarV is evaluated in comparison with state-of-the-art in many challenging benchmarks. The experiments show that StarV outperforms existing tools in many aspects, such as timing performance, scalability, and memory consumption.

External IDs:dblp:conf/cav/TranCLLOHF25