A Systematic Mapping Study on the Verification of Cyber-Physical Systems

Duan, Pengfei; Zhou, Ying; Gong, Xufang; Li, Bixin

doi:10.1109/access.2018.2872015

Cited by 5 publications

(1 citation statement)

References 89 publications

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“…To prevent these scenarios, CPS engineers extensively test their systems to detect safety-critical software failures [8,29,38,99]. This activity is facilitated by automated testing tools (e.g., [4,10,62,67]) that are regularly used in safety critical CPS domains, including automotive (e.g., [62]), aerospace (e.g., [67]), and medical (e.g., [57]).…”

Section: Introductionmentioning

confidence: 99%

Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions

Formica¹,

Askarpour²,

Menghi³

2022

Preprint

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Search-based software testing (SBST) typically relies on fitness functions to guide the search exploration toward software failures. There are two main techniques to define fitness functions: (a) automated fitness function computation from the specification of the system requirements and (b) manual fitness function design. Both techniques have advantages. The former uses information from the system requirements to guide the search toward portions of the input domain that are more likely to contain failures. The latter uses the engineers' domain knowledge.We propose ATheNA, a novel SBST framework that combines fitness functions that are automatically generated from requirements specifications and manually defined by engineers. We design and implement ATheNA-S, an instance of ATheNA that targets Simulink ® models. We evaluate ATheNA-S by considering a large set of models and requirements from different domains. We compare our solution with an SBST baseline tool that supports automatically generated fitness functions, and another one that supports manually defined fitness functions. Our results show that ATheNA-S generates more failure-revealing test cases than the baseline tools and that the difference between the performance of ATheNA-S and the baseline tools is not statistically significant. We also assess whether ATheNA-S could generate failure-revealing test cases when applied to a large case study from the automotive domain. Our results show that ATheNA-S successfully revealed a requirement violation in our case study.

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