An optimized test case generation technique for enhancing state-sensitivity partitioning

Software testing is a vital phase in software development life cycle (SDLC) and its principal element is test case. Test case generation remains the most dominant the research area in software testing. One of the techniques that were proposed for generating test cases is State Sensitivity Partiti...

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Main Author:	Sultan, Ammar Mohammed Dawood
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Genetic algorithms Programming
Online Access:	http://psasir.upm.edu.my/id/eprint/68748/1/FSKTM%202018%209%20IR.pdf
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spelling	my-upm-ir.687482019-05-29T01:37:33Z An optimized test case generation technique for enhancing state-sensitivity partitioning 2017-11 Sultan, Ammar Mohammed Dawood Software testing is a vital phase in software development life cycle (SDLC) and its principal element is test case. Test case generation remains the most dominant the research area in software testing. One of the techniques that were proposed for generating test cases is State Sensitivity Partitioning (SSP). It aims to avoid the exhaustive testing of module’s entire states. It partitions the entire data states based on their sensitivities towards events, conditions and actions. The test data for SSP is in the form of event sequences. As there is no limit on the number of events that any sequence can hold, lengthy test cases might be generated. Besides, no constrains were applied in order to avoid retesting a component that was already tested. Subsequently, a state explosion might be encountered. The aim of this study was to address the problem of redundant states encountered within SSP test cases. An optimization technique was proposed, enSSP, featuring the generation of optimized test cases. The scope of this work is testing a module with memory where each module may consist of several programs. The essence of enSSP is to combine the features of Genetic Algorithm (GA) with a suite reduction technique to achieve optimization. GA removes redundant states from test cases while the reduction technique removes redundant sequences from the suite. Afterwards, a prioritization algorithm used for sorting the test cases so the first test case detects the highest number of mutants followed by the cases that kill its live mutants. Experiments were conducted using mutation analysis to compare the fault detection capabilities of enSSP and SSP. The main interest of the experiment is to demonstrate the capability of enSSP. With respect to both quality attributes, the effectiveness and the efficiency, the results indicate that enSSP is more effective and efficient than SSP. Genetic algorithms Programming 2017-11 Thesis http://psasir.upm.edu.my/id/eprint/68748/ http://psasir.upm.edu.my/id/eprint/68748/1/FSKTM%202018%209%20IR.pdf text en public doctoral Universiti Putra Malaysia Genetic algorithms Programming
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Genetic algorithms Programming
spellingShingle	Genetic algorithms Programming Sultan, Ammar Mohammed Dawood An optimized test case generation technique for enhancing state-sensitivity partitioning
description	Software testing is a vital phase in software development life cycle (SDLC) and its principal element is test case. Test case generation remains the most dominant the research area in software testing. One of the techniques that were proposed for generating test cases is State Sensitivity Partitioning (SSP). It aims to avoid the exhaustive testing of module’s entire states. It partitions the entire data states based on their sensitivities towards events, conditions and actions. The test data for SSP is in the form of event sequences. As there is no limit on the number of events that any sequence can hold, lengthy test cases might be generated. Besides, no constrains were applied in order to avoid retesting a component that was already tested. Subsequently, a state explosion might be encountered. The aim of this study was to address the problem of redundant states encountered within SSP test cases. An optimization technique was proposed, enSSP, featuring the generation of optimized test cases. The scope of this work is testing a module with memory where each module may consist of several programs. The essence of enSSP is to combine the features of Genetic Algorithm (GA) with a suite reduction technique to achieve optimization. GA removes redundant states from test cases while the reduction technique removes redundant sequences from the suite. Afterwards, a prioritization algorithm used for sorting the test cases so the first test case detects the highest number of mutants followed by the cases that kill its live mutants. Experiments were conducted using mutation analysis to compare the fault detection capabilities of enSSP and SSP. The main interest of the experiment is to demonstrate the capability of enSSP. With respect to both quality attributes, the effectiveness and the efficiency, the results indicate that enSSP is more effective and efficient than SSP.
format	Thesis
qualification_level	Doctorate
author	Sultan, Ammar Mohammed Dawood
author_facet	Sultan, Ammar Mohammed Dawood
author_sort	Sultan, Ammar Mohammed Dawood
title	An optimized test case generation technique for enhancing state-sensitivity partitioning
title_short	An optimized test case generation technique for enhancing state-sensitivity partitioning
title_full	An optimized test case generation technique for enhancing state-sensitivity partitioning
title_fullStr	An optimized test case generation technique for enhancing state-sensitivity partitioning
title_full_unstemmed	An optimized test case generation technique for enhancing state-sensitivity partitioning
title_sort	optimized test case generation technique for enhancing state-sensitivity partitioning
granting_institution	Universiti Putra Malaysia
publishDate	2017
url	http://psasir.upm.edu.my/id/eprint/68748/1/FSKTM%202018%209%20IR.pdf
_version_	1747812627021561856

An optimized test case generation technique for enhancing state-sensitivity partitioning

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