Fast Automatic Verification of Large-Scale Systems with Lookup Tables

Title	Fast Automatic Verification of Large-Scale Systems with Lookup Tables
Authors	Nikos Arichega, Sumanth Dathathri, Shashank Vernekar, Sicun Gao, Shin’Ichi Shiraishi and Richard M. Murray
Source	Submitted, 2017 ACM International Conference on Hybrid Systems: Computation and Control (HSCC)
Abstract	Modern safety-critical systems are difficult to formally verify, largely due to their large scale. In particular, the widespread use of lookup tables in embedded systems across diverse industries, such as aeronautics and automotive systems, create a critical obstacle to the scala- bility of formal verification. This paper presents a novel approach for the formal verification of large-scale systems with lookup tables. We use a learning-based technique to automatically learn abstractions of the lookup tables and use the abstractions to then prove the desired property. If the verification fails, we propose a falsification heuristic to search for a violation of the specification. In contrast with previous work on lookup table verification, our technique is completely automatic, making it ideal for deployment in a production environment. To our knowledge, our approach is the only technique that can automatically verify large-scale systems lookup with tables. We illustrate the effectiveness of our technique on a benchmark which cannot be handled by the commer- cial tool SLDV, and we demonstrate the performance improvement provided by our technique.
Type	Conference paper
URL	http://www.cds.caltech.edu/~murray/preprints/ari+17-hscc s.pdf
DOI
Tag	ari+17-hscc
ID	2016h
Funding
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