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Genetic algorithm application for enhancing state-sensitivity partitioning


Citation

Mohammed Sultan, Ammar and Baharom, Salmi and Abd Ghani, Abdul Azim and Din, Jamilah and Zulzalil, Hazura (2015) Genetic algorithm application for enhancing state-sensitivity partitioning. In: Testing Software and System: 27th IFIP WG 6.1 International Conference, ICTSS 2015, Sharjah and Dubai, United Arab Emirates, November 23-25, 2015, Proceedings. Lecture Notes in Computer Science (9447). Springer International Publishing, Dubai, UAE, pp. 249-256. ISBN 9783319259444; EISBN: 9783319259451

Abstract / Synopsis

Software testing is the most crucial phase in software development life cycle which intends tofind faults as much as possible. Test case generation leads the research in software testing. So, many techniques were proposed for the sake of automating the test case generation process. State sensitivity partitioning is a technique that partitions the entire states of a module. The generated test cases are composed of sequences of events. However, there is an infinite set of sequences with no upper bound on the length of a sequence. Thus, a lengthy test sequence might be encountered with redundant data states, which will increase the size of test suite and, consequently, the process of testing will be ineffective. Therefore, there is a need to optimize those test cases generated by SSP. GA has been identified as the most common potential technique amongseveral optimization techniques. Thus, GA is investigated to integrate it with the existing SSP. This paper addresses the issue on deriving the fitness function for optimizing the sequence of events produced by SSP.


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Additional Metadata

Item Type: Book Section
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1007/978-3-319-25945-1_16
Publisher: Springer International Publishing
Keywords: Genetic Algorithm (GA); State-Sensitivity partitioning (SSP); Test case; Sequence of events; Data state
Depositing User: Azhar Abdul Rahman
Date Deposited: 27 Jun 2016 14:24
Last Modified: 27 Jun 2016 14:46
Altmetrics: http://www.almetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1007/978-3-319-25945-1_16
URI: http://psasir.upm.edu.my/id/eprint/47161
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