Data Driven Testing of Cyber Physical Systems

Humeniuk, Dmytro; Antoniol, Giuliano; Khomh, Foutse

doi:10.1109/sbst52555.2021.00010

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…A variety of manually defined fitness functions was proposed in the literature. Manually defined fitness functions can guide the search to produce test outputs with diverse shapes [63], maximize or minimize the outputs of a system characterizing its critical behavior [20], maximize diversity in output signals [64], quantify the difference between a reference and an output signal [15], and minimize the difference between the expected and simulated behaviour of a CPS [46]. Many manually defined fitness functions were also proposed to guide SBST frameworks that analyze software code.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

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

Formica¹,

Askarpour²,

Menghi³

2022

Preprint

View full text Add to dashboard Cite

show abstract

Towards Smarter, Interconnected Futures: The Crucial Role of Data in Cyber-Physical Systems

Pannerselvam,

Rajiakodi

2024

Intelligent Cyber-Physical Systems for Healthcare Solutions

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

Formica,

Fan,

Menghi

2023

ACM Trans. Softw. Eng. Methodol.

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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 more likely to contain failures. The latter uses the engineers’ domain knowledge. We propose ATheNA , a novel SBST framework that combines fitness functions automatically generated from requirements specifications and those 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 from different domains. Our results show that ATheNA-S generates more failure-revealing test cases than existing baseline tools and that the difference between the runtime 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 two representative case studies: one from the automotive domain and one from the medical domain. Our results show that ATheNA-S successfully revealed a requirement violation in our case studies.

show abstract

Data Driven Testing of Cyber Physical Systems

Cited by 4 publications

References 12 publications

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

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

Towards Smarter, Interconnected Futures: The Crucial Role of Data in Cyber-Physical Systems

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

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