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Testing Cyber-Physical Systems through Bayesian Optimization

Published: 27 September 2017 Publication History

Abstract

Many problems in the design and analysis of cyber-physical systems (CPS) reduce to the following optimization problem: given a CPS which transforms continuous-time input traces in Rm to continuous-time output traces in Rn and a cost function over output traces, find an input trace which minimizes the cost. Cyber-physical systems are typically so complex that solving the optimization problem analytically by examining the system dynamics is not feasible. We consider a black-box approach, where the optimization is performed by testing the input-output behaviour of the CPS.
We provide a unified, tool-supported methodology for CPS testing and optimization. Our tool is the first CPS testing tool that supports Bayesian optimization. It is also the first to employ fully automated dimensionality reduction techniques. We demonstrate the potential of our tool by running experiments on multiple industrial case studies. We compare the effectiveness of Bayesian optimization to state-of-the-art testing techniques based on CMA-ES and Simulated Annealing.

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Information & Contributors

Information

Published In

cover image ACM Transactions on Embedded Computing Systems
ACM Transactions on Embedded Computing Systems  Volume 16, Issue 5s
Special Issue ESWEEK 2017, CASES 2017, CODES + ISSS 2017 and EMSOFT 2017
October 2017
1448 pages
ISSN:1539-9087
EISSN:1558-3465
DOI:10.1145/3145508
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 27 September 2017
Accepted: 01 July 2017
Revised: 01 June 2017
Received: 01 April 2017
Published in TECS Volume 16, Issue 5s

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Author Tags

  1. Bayesian optimization
  2. CMA-ES
  3. Cyber-physical systems
  4. Gaussian processes
  5. Simulated Annealing
  6. black-box optimization
  7. dimensionality reduction
  8. testing

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • ERC Synergy Award “IMPACT”
  • Toyota

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Cited By

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  • (2024)Falsification using Reachability of Surrogate Koopman ModelsProceedings of the 27th ACM International Conference on Hybrid Systems: Computation and Control10.1145/3641513.3650141(1-13)Online publication date: 14-May-2024
  • (2024)Approximating the Geometry of Temporal Logic FormulasProceedings of the 27th ACM International Conference on Hybrid Systems: Computation and Control10.1145/3641513.3650139(1-10)Online publication date: 14-May-2024
  • (2024)Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics ManipulationProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639740(263-274)Online publication date: 14-Apr-2024
  • (2024)Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation With Probabilistic GuaranteesIEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.329798421:3(4504-4525)Online publication date: Jul-2024
  • (2024)BEACON: A Bayesian Evolutionary Approach for Counterexample Generation of Control SystemsIEEE Access10.1109/ACCESS.2024.343651512(106455-106465)Online publication date: 2024
  • (2024)Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and VerificationArtificial Intelligence10.1016/j.artint.2024.104195(104195)Online publication date: Aug-2024
  • (2024)Scenario-Based Flexible Modeling and Scalable Falsification for Reconfigurable CPSsComputer Aided Verification10.1007/978-3-031-65633-0_15(329-355)Online publication date: 24-Jul-2024
  • (2023)Counterexample-Guided Policy Refinement in Multi-Agent Reinforcement LearningProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598816(1606-1614)Online publication date: 30-May-2023
  • (2023)Intelligent Embedded Systems Platform for Vehicular Cyber-Physical SystemsElectronics10.3390/electronics1213290812:13(2908)Online publication date: 2-Jul-2023
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