Analyzing neighborhoods of falsifying traces in cyber-physical systems

Abstract

We study the problem of analyzing falsifying traces of cyber-physical systems. Speci cally, given a system model and an input which is a counterexample to a property of interest, we wish to understand which parts of the inputs are "responsible" for the counterexample as a whole. Whereas this problem is well known to be hard to solve precisely, we provide an approach based on learning from repeated simulations of the system under test.Our approach generalizes the classic concept of "one-at-a-time" sensitivity analysis used in the risk and decision analysis community to understand how inputs to a system in uence a property in question. Speci cally, we pose the problem as one of nding a neighborhood of inputs that contains the falsifying counterexample in question, such that each point in this neighborhood corresponds to a falsifying input with a high probability. We use ideas from statistical hypothesis testing to infer and validate such neighborhoods from repeated simulations of the system under test.is approach not only helps to understand the sensitivity of these counterexamples to various parts of the inputs, but also generalizes or widens the given counterexample by returning a neighborhood of counterexamples around it.We demonstrate our approach on a series of increasingly complex examples from automotive and closed loop medical device domains. We also compare our approach against related techniques based on regression and machine learning. CCS CONCEPTS•Computer systems organization →Embedded and cyberphysical systems; •Mathematics of computing →Hypothesis testing and con dence interval computation;

Metamathematics for Systems Design

“…One thing we can do here is to polish up the method so that it will find solutions more often; the other thing is statistical inference that tells, based on the observed absence of solutions, the confidence with which we can conclude that no solutions exist. See [23] for an example of works in this direction. We shall combine results and observations in statistical model checking (Sect.…”

A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

“…Many of the existing safety validation tools are products of academic research and released as experimental prototypes. Although these tools are prototypes, several have matured enough to gain wide-spread usage and acceptance (Diwakaran et al, 2017;Dreossi et al, 2015;Tuncali et al, 2018;Zhang et al, 2019;Zutshi et al, 2014). Two particular falsification tools have become benchmark standards in the field: S-TaLiRo (G. Fainekos et al, 2019) and Breach (Donzé, 2010).…”

CPSDebug: Automatic failure explanation in CPS models

“…Finally, we mention the recent work of analyzing neighborhoods of falsifying traces in CPS [11] where the authors use search-based testing to characterize how "robust" is the falsifying trace. This provides an alternative and complementary way of understanding properties of the violation.…”