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An Active Learning Approach to the Falsification of Black Box Cyber-Physical Systems

  • Simone Silvetti
  • Alberto Policriti
  • Luca Bortolussi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10510)

Abstract

Search-based testing is widely used to find bugs in models of complex Cyber-Physical Systems. Latest research efforts have improved this approach by casting it as a falsification procedure of formally specified temporal properties, exploiting the robustness semantics of Signal Temporal Logic. The scaling of this approach to highly complex engineering systems requires efficient falsification procedures, which should be applicable also to black box models. Falsification is also exacerbated by the fact that inputs are often time-dependent functions. We tackle the falsification of formal properties of complex black box models of Cyber-Physical Systems, leveraging machine learning techniques from the area of Active Learning. Tailoring these techniques to the falsification problem with time-dependent, functional inputs, we show a considerable gain in computational effort, by reducing the number of model simulations needed. The effectiveness of the proposed approach is discussed on a challenging industrial-level benchmark from automotive.

Keywords

Model-based testing Robustness Gaussian Processes Cyber-Physical Systems Falsification 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Simone Silvetti
    • 1
    • 2
  • Alberto Policriti
    • 2
    • 3
  • Luca Bortolussi
    • 4
    • 5
    • 6
  1. 1.Esteco SpATriesteItaly
  2. 2.DIMAUniversity of UdineUdineItaly
  3. 3.Istituto di Genomica ApplicataUdineItaly
  4. 4.DMGUniversity of TriesteTriesteItaly
  5. 5.Modelling and Simulation GroupSaarland UniversitySaarbrückenGermany
  6. 6.CNR-ISTIPisaItaly

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