Randomized Reachability Analysis in Uppaal: Fast Error Detection in Timed Systems

Andrej Kiviriga*, Kim Guldstrand Larsen, Ulrik Nyman

*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

1 Citationer (Scopus)
97 Downloads (Pure)

Abstract

We introduce Randomized Reachability Analysis – an efficient and highly scalable method for detection of “rare event” states, such as errors. Due to the under-approximate nature of the method, it excels at quick falsification of models and can greatly improve the model-based development process: using lightweight randomized methods early in the development for the discovery of bugs, followed by expensive symbolic verification only at the very end. We show the scalability of our method on a number of Timed Automata and Stopwatch Automata models of varying sizes and origin. Among them, we revisit the schedulability problem from the Herschel-Planck industrial case study, where our new method finds the deadline violation three orders of magnitude faster: some cases could previously be analyzed by statistical model checking (SMC) in 23 h and can now be checked in 23 s. Moreover, a deadline violation is discovered in a number of cases that where previously intractable. We have implemented the Randomized Reachability Analysis – and made it available – in the tool Uppaal.

OriginalsprogEngelsk
TitelFormal Methods for Industrial Critical Systems : 26th International Conference, FMICS 2021, Proceedings
RedaktørerAlberto Lluch Lafuente, Anastasia Mavridou
Antal sider18
ForlagSpringer Science+Business Media
Publikationsdato2021
Sider149-166
ISBN (Trykt)9783030852474
DOI
StatusUdgivet - 2021
Begivenhed26th International Conference on Formal Methods for Industrial Critical Systems, FMICS 2021 - Virtual, Online
Varighed: 24 aug. 202126 aug. 2021

Konference

Konference26th International Conference on Formal Methods for Industrial Critical Systems, FMICS 2021
ByVirtual, Online
Periode24/08/202126/08/2021
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind12863 LNCS
ISSN0302-9743

Bibliografisk note

Funding Information:
Supported by the ERC Advanced Grant Project: LASSO: Learning, Analysis, Synthesis and Optimization of Cyber-Physical Systems, and by the Villum Investigator project S4OS: Synthesis of Safe, Small, Secure and Optimal Strategies for Cyber-Physical Systems.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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