Enhanced non-dominated sorting genetic algorithm for test case optimization

Due to inevitable software changes, regression testing has become a crucial phase in software development process. Many software testers and researchers agreed that regression testing process consumes more time and cost during software development. Test case optimization has become one of the best s...

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Main Author:	Mohd. Ismail, Izwan
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/81036/1/IzwanMohdIsmailMFC2018.pdf
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spelling	my-utm-ep.810362019-07-24T03:06:15Z Enhanced non-dominated sorting genetic algorithm for test case optimization 2018-06 Mohd. Ismail, Izwan QA75 Electronic computers. Computer science Due to inevitable software changes, regression testing has become a crucial phase in software development process. Many software testers and researchers agreed that regression testing process consumes more time and cost during software development. Test case optimization has become one of the best solutions to overcome problems in regression testing. Test case optimization is focusing on reducing number of test cases in the test suite that may reduce the overall testing time, cost and effort of software testers. It considers multiple objectives and provides several numbers of optimal solution based on objectives of the testing. Therefore, this research aims at developing an alternative solution of test case optimization technique using NSGA II with fitness scaling as an additional function. Fitness scaling function is applied in NSGA II to eliminate pre-mature convergence among set of solution in the evolution of offspring in NSGA II which may produce more efficient fitness value. This research focuses on regression testing optimization by implementing weight of test cases and fault detection rate per test case as its objective function for optimization purposes. The proposed technique is applied to the GUI-based testing case study. The result shows that Pareto front produced by enhanced NSGA II give more wider set of solution that contains more alternatives and provide better trade-off among solutions. The evaluation shows that enhanced NSGA II perform better compared to conventional NSGA II by increasing the percentage of the reduced test cases with 25% and yield lower fault detection loss with 1.64% which indicating that set of reduced test cases using enhanced NSGA II is able to maintain the fault detection capability in the system under test. 2018-06 Thesis http://eprints.utm.my/id/eprint/81036/ http://eprints.utm.my/id/eprint/81036/1/IzwanMohdIsmailMFC2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:119433 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mohd. Ismail, Izwan Enhanced non-dominated sorting genetic algorithm for test case optimization
description	Due to inevitable software changes, regression testing has become a crucial phase in software development process. Many software testers and researchers agreed that regression testing process consumes more time and cost during software development. Test case optimization has become one of the best solutions to overcome problems in regression testing. Test case optimization is focusing on reducing number of test cases in the test suite that may reduce the overall testing time, cost and effort of software testers. It considers multiple objectives and provides several numbers of optimal solution based on objectives of the testing. Therefore, this research aims at developing an alternative solution of test case optimization technique using NSGA II with fitness scaling as an additional function. Fitness scaling function is applied in NSGA II to eliminate pre-mature convergence among set of solution in the evolution of offspring in NSGA II which may produce more efficient fitness value. This research focuses on regression testing optimization by implementing weight of test cases and fault detection rate per test case as its objective function for optimization purposes. The proposed technique is applied to the GUI-based testing case study. The result shows that Pareto front produced by enhanced NSGA II give more wider set of solution that contains more alternatives and provide better trade-off among solutions. The evaluation shows that enhanced NSGA II perform better compared to conventional NSGA II by increasing the percentage of the reduced test cases with 25% and yield lower fault detection loss with 1.64% which indicating that set of reduced test cases using enhanced NSGA II is able to maintain the fault detection capability in the system under test.
format	Thesis
qualification_level	Master's degree
author	Mohd. Ismail, Izwan
author_facet	Mohd. Ismail, Izwan
author_sort	Mohd. Ismail, Izwan
title	Enhanced non-dominated sorting genetic algorithm for test case optimization
title_short	Enhanced non-dominated sorting genetic algorithm for test case optimization
title_full	Enhanced non-dominated sorting genetic algorithm for test case optimization
title_fullStr	Enhanced non-dominated sorting genetic algorithm for test case optimization
title_full_unstemmed	Enhanced non-dominated sorting genetic algorithm for test case optimization
title_sort	enhanced non-dominated sorting genetic algorithm for test case optimization
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/81036/1/IzwanMohdIsmailMFC2018.pdf
_version_	1747818297350422528

Enhanced non-dominated sorting genetic algorithm for test case optimization

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