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Software Engineering Research, Management and Applications pp 15–25Cite as

Optimized Test Case Generation Based on Operational Profiles with Fault-Proneness Information

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 578))

Abstract

In this paper, a novel formal model called an OPFPI (operational profile with fault-proneness information) and a novel algorithm to generate optimized test cases from the OPFPI are proposed in order to effectively improve software reliability or gain a perspective of software reliability in a limited span of time for testing. The OPFPI includes the feasibility problem due to the use of guards (conditions to make specific state transitions feasible); therefore, ant colony optimization is employed in the algorithm to generate test cases that cover frequent and fault-prone state transitions as comprehensively as possible. A test tool that implements the OPFPI and executes the optimized test case generation has been developed, and it has been applied to a non-trivial system. The obtained results indicate that significant improvement of test cases can be achieved in a short time.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 23700038.

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Authors and Affiliations

  1. Faculty of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu-shi, Kagawa, 761-0396, Japan

    Tomohiko Takagi

  2. Faculty of Engineering, Department of Computer Engineering, Yaşar University, Üniversite Caddesi, No: 37–39, Ağaçli Yol, Bornova, Izmir, Turkey

    Mutlu Beyazıt

Authors
  1. Tomohiko Takagi
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  2. Mutlu Beyazıt
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Corresponding author

Correspondence to Tomohiko Takagi .

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Editors and Affiliations

  1. Software Engineering and Information Technology Institute, Central Michigan University, Mount Pleasant, Michigan, USA

    Roger Lee

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© 2015 Springer International Publishing Switzerland

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Takagi, T., Beyazıt, M. (2015). Optimized Test Case Generation Based on Operational Profiles with Fault-Proneness Information. In: Lee, R. (eds) Software Engineering Research, Management and Applications. Studies in Computational Intelligence, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-11265-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-11265-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11264-0

  • Online ISBN: 978-3-319-11265-7

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