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Model-based mutation testing - Approach and case studies

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Date

2016

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier B.V.

Open Access Color

Hybrid

Green Open Access

Yes

OpenAIRE Downloads

117

OpenAIRE Views

65

Publicly Funded

No
Impulse
Top 10%
Influence
Average
Popularity
Top 10%

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Journal Issue

Abstract

This paper rigorously introduces the concept of model-based mutation testing (MBMT) and positions it in the landscape of mutation testing. Two elementary mutation operators, insertion and omission, are exemplarily applied to a hierarchy of graph-based models of increasing expressive power including directed graphs, event sequence graphs, finite-state machines and statecharts. Test cases generated based on the mutated models (mutants) are used to determine not only whether each mutant can be killed but also whether there are any faults in the corresponding system under consideration (SUC) developed based on the original model. Novelties of our approach are: (1) evaluation of the fault detection capability (in terms of revealing faults in the SUC) of test sets generated based on the mutated models, and (2) superseding of the great variety of existing mutation operators by iterations and combinations of the two proposed elementary operators. Three case studies were conducted on industrial and commercial real-life systems to demonstrate the feasibility of using the proposed MBMT approach in detecting faults in SUC, and to analyze its characteristic features. Our experimental data suggest that test sets generated based on the mutated models created by insertion operators are more effective in revealing faults in SUC than those generated by omission operators. Worth noting is that test sets following the MBMT approach were able to detect faults in the systems that were tested by manufacturers and independent testing organizations before they were released. © 2016 Elsevier B.V. All rights reserved.

Description

Keywords

Fault detection capability, Model-based mutation testing, Model-based testing, Mutation operator, Mutation testing, Fault detection, Software testing

Fields of Science

02020701 Software engineering/Computer occupations, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 020207 software engineering, 02 engineering and technology, 02020102 Mathematical optimization/Evolutionary algorithms

Citation

62

WoS Q

N/A

Scopus Q

N/A
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OpenCitations Citation Count
45

Source

Science of Computer Programming

Volume

120

Issue

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Citations

CrossRef : 17

Scopus : 68

Captures

Mendeley Readers : 70

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