HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Genetic Algorithm Application for Enhancing State-Sensitivity Partitioning

Abstract : Software testing is the most crucial phase in software development life cycle which intends to find 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 among several 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.
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download

Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, February 17, 2017 - 10:25:36 AM
Last modification on : Friday, February 17, 2017 - 10:37:07 AM
Long-term archiving on: : Thursday, May 18, 2017 - 2:10:28 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Ammar Sultan, Salmi Baharom, Abdul Ghani, Jamilah Din, Hazura Zulzalil. Genetic Algorithm Application for Enhancing State-Sensitivity Partitioning. 27th IFIP International Conference on Testing Software and Systems (ICTSS), Nov 2015, Sharjah and Dubai, United Arab Emirates. pp.249-256, ⟨10.1007/978-3-319-25945-1_16⟩. ⟨hal-01470151⟩



Record views


Files downloads