DryVR: Data-Driven Verification and Compositional Reasoning for Automotive Systems

Article Details
Title:	DryVR: Data-Driven Verification and Compositional Reasoning for Automotive Systems
Article URLs:	https://doi.org/10.1007/978-3-319-63387-9_22
Alternative Article URLs:	https://dl.acm.org/citation.cfm?doid=3178126.3187008 https://arxiv.org/abs/1702.06902
Authors:	Chuchu Fan	University of Illinois at Urbana-Champaign, Electrical and Computer Engineering
	Bolun Qi	University of Illinois at Urbana-Champaign, Electrical and Computer Engineering
	Sayan Mitra	University of Illinois at Urbana-Champaign, Electrical and Computer Engineering
	Mahesh Viswanathan	University of Illinois at Urbana-Champaign, Computer Science
Sharing:	Research produced artifacts
Verification:	Authors have verified information
Artifact Evaluation Badge:	Artifacts for some papers are reviewed by an artifact evaluation, reproducibility, or similarly named committee. This is one such paper that passed review. awarded
Artifact URLs:	https://github.com/qibolun/DryVR https://github.com/qibolun/DryVR_0.2
Artifact Correspondence Email Addresses:
NSF Award Numbers:	1054247, 1422798
DBLP Key:	conf/cav/FanQM017
Author Comments:	DryVR is an open source tool for verification of hybrid systems with black-box components

Computer Aided Verification, CAV 2017