Authors
Tomohiko [email protected]
Faculty of Engineering and Design, Kagawa University, 2217-20 Hayashi-cho, Takamatsu-shi, Kagawa 761-0396, Japan
Shogo [email protected]
Graduate School of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu-shi, Kagawa 761-0396, Japan
Available Online 30 September 2018.
DOI
https://doi.org/10.2991/jrnal.2018.5.2.11How to use a DOI?
Keywords
Mutation Testing; Model-Based Testing; Place/Transition Net; Genetic Algorithm
Abstract
An EPN (Extended Place/transition Net) is used as a formal model that represents the behavior of software. When mutation testing is performed based on the EPN, failures are intentionally inserted into an original EPN (EPN that represents the expected behavior of software) in order to create mutant EPNs. A large number of higher-quality mutant EPNs are needed to expect the higher degree of accuracy for a mutation score, but the techniques to generate them have not been established. To address this problem, we construct a technique to generate mutant EPNs, and develop a tool to support the technique. In this technique based on a genetic algorithm, a set of mutant EPNs corresponds to a chromosome, and the fitness of each chromosome is evaluated based on an original EPN weighted by metrics. This paper shows the procedure of this technique, the functions of the tool, and the discussion about its effectiveness.

Copyright
Copyright © 2018, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).