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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
Online Access:http://psasir.upm.edu.my/id/eprint/68748/1/FSKTM%202018%209%20IR.pdf
http://psasir.upm.edu.my/id/eprint/68748/
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spelling my.upm.eprints.687482019-05-29T01:37:33Z http://psasir.upm.edu.my/id/eprint/68748/ An optimized test case generation technique for enhancing state-sensitivity partitioning 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. 2017-11 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/68748/1/FSKTM%202018%209%20IR.pdf Sultan, Ammar Mohammed Dawood (2017) An optimized test case generation technique for enhancing state-sensitivity partitioning. PhD thesis, Universiti Putra Malaysia.
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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
author Sultan, Ammar Mohammed Dawood
spellingShingle Sultan, Ammar Mohammed Dawood
An optimized test case generation technique for enhancing state-sensitivity partitioning
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
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/68748/1/FSKTM%202018%209%20IR.pdf
http://psasir.upm.edu.my/id/eprint/68748/
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score 13.211869

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