Automated pairwise testing approach based on classification tree modeling and negative selection algorithm

Generating the test cases for analysis is an important activity in software testing to increase the trust level of users. The traditional way to generate test cases is called exhaustive testing. It is infeasible and time consuming because it generates too many numbers of test cases. A combinatorial...

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Main Author:	Sandin, Easter Viviana
Format:	Thesis
Language:	English
Published:	2019
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/98282/1/EasterVivianaSandinMSC2019.pdf
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spelling	my-utm-ep.982822022-12-04T10:05:03Z Automated pairwise testing approach based on classification tree modeling and negative selection algorithm 2019 Sandin, Easter Viviana QA Mathematics QA75 Electronic computers. Computer science Generating the test cases for analysis is an important activity in software testing to increase the trust level of users. The traditional way to generate test cases is called exhaustive testing. It is infeasible and time consuming because it generates too many numbers of test cases. A combinatorial testing was used to solve the exhaustive testing problem. The popular technique in combinatorial testing is called pairwise testing that involves the interaction of two parameters. Although pairwise testing can cover the exhaustive testing problems, there are several issues that should be considered. First issue is related to modeling of the system under test (SUT) as a preprocess for test case generation as it has yet to be implemented in automated proposed approaches. The second issue is different approaches generate different number of test cases for different covering arrays. These issues showed that there is no one efficient way to find the optimal solution in pairwise testing that would consider the invalid combination or constraint. Therefore, a combination of Classification Tree Method and Negative Selection Algorithm (CTM-NSA) was developed in this research. The CTM approach was revised and enhanced to be used as the automated modeling and NSA approach was developed to optimize the pairwise testing by generate the low number of test cases. The findings showed that the CTM-NSA outperformed the other modeling method in terms of easing the tester and generating a low number of test cases in the small SUT size. Furthermore, it is comparable to the efficient approaches as compared to many of the test case generation approaches in large SUT size as it has good characteristic in detecting the self and non-self-sample. This characteristic occurs during the detection stage of NSA by covering the best combination of values for all parameters and considers the invalid combinations or constraints in order to achieve a hundred percent pairwise testing coverage. In addition, validation of the approach was performed using Statistical Wilcoxon Signed-Rank Test. Based on these findings, CTM-NSA had been shown to be able perform modeling in an automated way and achieve the minimum or a low number of test cases in small SUT size. 2019 Thesis http://eprints.utm.my/id/eprint/98282/ http://eprints.utm.my/id/eprint/98282/1/EasterVivianaSandinMSC2019.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:144588 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics QA Mathematics
spellingShingle	QA Mathematics QA Mathematics Sandin, Easter Viviana Automated pairwise testing approach based on classification tree modeling and negative selection algorithm
description	Generating the test cases for analysis is an important activity in software testing to increase the trust level of users. The traditional way to generate test cases is called exhaustive testing. It is infeasible and time consuming because it generates too many numbers of test cases. A combinatorial testing was used to solve the exhaustive testing problem. The popular technique in combinatorial testing is called pairwise testing that involves the interaction of two parameters. Although pairwise testing can cover the exhaustive testing problems, there are several issues that should be considered. First issue is related to modeling of the system under test (SUT) as a preprocess for test case generation as it has yet to be implemented in automated proposed approaches. The second issue is different approaches generate different number of test cases for different covering arrays. These issues showed that there is no one efficient way to find the optimal solution in pairwise testing that would consider the invalid combination or constraint. Therefore, a combination of Classification Tree Method and Negative Selection Algorithm (CTM-NSA) was developed in this research. The CTM approach was revised and enhanced to be used as the automated modeling and NSA approach was developed to optimize the pairwise testing by generate the low number of test cases. The findings showed that the CTM-NSA outperformed the other modeling method in terms of easing the tester and generating a low number of test cases in the small SUT size. Furthermore, it is comparable to the efficient approaches as compared to many of the test case generation approaches in large SUT size as it has good characteristic in detecting the self and non-self-sample. This characteristic occurs during the detection stage of NSA by covering the best combination of values for all parameters and considers the invalid combinations or constraints in order to achieve a hundred percent pairwise testing coverage. In addition, validation of the approach was performed using Statistical Wilcoxon Signed-Rank Test. Based on these findings, CTM-NSA had been shown to be able perform modeling in an automated way and achieve the minimum or a low number of test cases in small SUT size.
format	Thesis
qualification_level	Master's degree
author	Sandin, Easter Viviana
author_facet	Sandin, Easter Viviana
author_sort	Sandin, Easter Viviana
title	Automated pairwise testing approach based on classification tree modeling and negative selection algorithm
title_short	Automated pairwise testing approach based on classification tree modeling and negative selection algorithm
title_full	Automated pairwise testing approach based on classification tree modeling and negative selection algorithm
title_fullStr	Automated pairwise testing approach based on classification tree modeling and negative selection algorithm
title_full_unstemmed	Automated pairwise testing approach based on classification tree modeling and negative selection algorithm
title_sort	automated pairwise testing approach based on classification tree modeling and negative selection algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing
granting_department	Faculty of Engineering - School of Computing
publishDate	2019
url	http://eprints.utm.my/id/eprint/98282/1/EasterVivianaSandinMSC2019.pdf
_version_	1776100573867671552

Automated pairwise testing approach based on classification tree modeling and negative selection algorithm

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