Weighted string distance approach based on modified clustering technique for optimizing test case prioritization

Numerous test case prioritization (TCP) approaches have been introduced to enhance the test viability in software testing activity with the goal to maximize early average percentage fault detection (APFD). String based approach had shown that applying a single string distance-based metric to differe...

Full description

Saved in:

Bibliographic Details
Main Author:	Khatibsyarbini, Muhammad
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/101584/1/MuhammadKhatibsyarbiniPSC2022.pdf.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.101584
record_format	uketd_dc
spelling	my-utm-ep.1015842023-06-26T06:47:35Z Weighted string distance approach based on modified clustering technique for optimizing test case prioritization 2022 Khatibsyarbini, Muhammad QA75 Electronic computers. Computer science Numerous test case prioritization (TCP) approaches have been introduced to enhance the test viability in software testing activity with the goal to maximize early average percentage fault detection (APFD). String based approach had shown that applying a single string distance-based metric to differentiate the test cases can improve the APFD and coverage rate (CR) results. However, to precisely differentiate the test cases in regression testing, the string approach still requires an enhancement as it lacks priority criteria. Therefore, a study on how to effectively cluster and prioritize test cases through string-based approach is conducted. To counter the string distances problem, weighted string distances is introduced. A further enhancement was made by tuning the weighted string metric with K-Means clustering and prioritization using Firefly Algorithm (FA) technique for the TCP approach to become more flexible in manipulating available information. Then, the combination of the weighted string distances along with clustering and prioritization is executed under the designed process for a new weighted string distances-based approach for complete evaluation. The experimental results show that all the weighted string distances obtained better results compared to its single string metric with average APFD values 95.73% and CR values 61.80% in cstcas Siemen dataset. As for the proposed weighted string distances approach with clustering techniques for regression testing, the combination obtained better results and flexibility than the conventional string approach. In addition, the proposed approach also passed statistical assessment by obtaining p-value higher than 0.05 in Shapiro-Wilk’s normality test and p-value lower than 0.05 in Tukey Kramer Post Hoc tests. In conclusion, the proposed weighted string distances approach improves the overall score of APFD and CE and provides flexibility in the TCP approach for regression testing environment. 2022 Thesis http://eprints.utm.my/id/eprint/101584/ http://eprints.utm.my/id/eprint/101584/1/MuhammadKhatibsyarbiniPSC2022.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150785 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Khatibsyarbini, Muhammad Weighted string distance approach based on modified clustering technique for optimizing test case prioritization
description	Numerous test case prioritization (TCP) approaches have been introduced to enhance the test viability in software testing activity with the goal to maximize early average percentage fault detection (APFD). String based approach had shown that applying a single string distance-based metric to differentiate the test cases can improve the APFD and coverage rate (CR) results. However, to precisely differentiate the test cases in regression testing, the string approach still requires an enhancement as it lacks priority criteria. Therefore, a study on how to effectively cluster and prioritize test cases through string-based approach is conducted. To counter the string distances problem, weighted string distances is introduced. A further enhancement was made by tuning the weighted string metric with K-Means clustering and prioritization using Firefly Algorithm (FA) technique for the TCP approach to become more flexible in manipulating available information. Then, the combination of the weighted string distances along with clustering and prioritization is executed under the designed process for a new weighted string distances-based approach for complete evaluation. The experimental results show that all the weighted string distances obtained better results compared to its single string metric with average APFD values 95.73% and CR values 61.80% in cstcas Siemen dataset. As for the proposed weighted string distances approach with clustering techniques for regression testing, the combination obtained better results and flexibility than the conventional string approach. In addition, the proposed approach also passed statistical assessment by obtaining p-value higher than 0.05 in Shapiro-Wilk’s normality test and p-value lower than 0.05 in Tukey Kramer Post Hoc tests. In conclusion, the proposed weighted string distances approach improves the overall score of APFD and CE and provides flexibility in the TCP approach for regression testing environment.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Khatibsyarbini, Muhammad
author_facet	Khatibsyarbini, Muhammad
author_sort	Khatibsyarbini, Muhammad
title	Weighted string distance approach based on modified clustering technique for optimizing test case prioritization
title_short	Weighted string distance approach based on modified clustering technique for optimizing test case prioritization
title_full	Weighted string distance approach based on modified clustering technique for optimizing test case prioritization
title_fullStr	Weighted string distance approach based on modified clustering technique for optimizing test case prioritization
title_full_unstemmed	Weighted string distance approach based on modified clustering technique for optimizing test case prioritization
title_sort	weighted string distance approach based on modified clustering technique for optimizing test case prioritization
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2022
url	http://eprints.utm.my/id/eprint/101584/1/MuhammadKhatibsyarbiniPSC2022.pdf.pdf
_version_	1776100732600057856

Weighted string distance approach based on modified clustering technique for optimizing test case prioritization

Similar Items