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

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 goodness of the proposed approach is discussed on a challenging industrial-level benchmark from automotive.

15 pages, 2 figures

Cited by in corpus (6)

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• Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning
• Active Fuzzing for Testing and Securing Cyber-Physical Systems
• A Logic for Monitoring Dynamic Networks of Spatially-distributed Cyber-Physical Systems
• Multi-Armed Bandits for Boolean Connectives in Hybrid System Falsification (Extended Version)
• Constraining Counterexamples in Hybrid System Falsification: Penalty-Based Approaches