Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms

Software development life cycle maintenance has been advocated as the critical part that consumes more time and resources. To understand the magnitude of the task to maintain the software product, software metrics have been used to make quantification based on their respective software features....

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Main Author:	Bakar, Abubakar Diwani
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Genetic algorithms Software measurement
Online Access:	http://psasir.upm.edu.my/id/eprint/69320/1/FSKTM%202016%208%20UPM%20IR.pdf
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spelling	my-upm-ir.693202022-03-10T06:48:24Z Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms 2016-03 Bakar, Abubakar Diwani Software development life cycle maintenance has been advocated as the critical part that consumes more time and resources. To understand the magnitude of the task to maintain the software product, software metrics have been used to make quantification based on their respective software features. To predict software maintainace, the proper metrics need to be selected to avoid the duplication or the outlying of the potential metrics. This is because on one hand, the individual metrics deals with only a single feature of the object-oriented systems, while on the other hand; the suites either contain duplicate metrics of the same goal or lack some important metrics that match the common attributes in the software products. The latest effort to solve this selection problem is the development of the metrics selection model that uses genetic algorithm (GA). However, the process failed to state clearly the encoding strategy in its initial stage. This thesis clarifies the issue using the objective method to develop the GA metric selection model for predicting the maintainability of object-oriented systems. The study proposes the use of software metric thresholds in the classification process during the GA representation. The software metric thresholds were used as indication for identifying unsafe design in software engineering. To evaluate this technique, an experiment was conducted on two geospatial systems developed using Java programming language where the Chidamber and Kemerer (CK) metrics were used. The proposed technique was also compared to the ranking results from the experts. The comparison results obtained when compared with those of Principal Component Analysis and the complete software metric suite were very promising. Moreover, the three techniques show significant differences in both treatments when compared using analysis of variance. Genetic algorithms Software measurement 2016-03 Thesis http://psasir.upm.edu.my/id/eprint/69320/ http://psasir.upm.edu.my/id/eprint/69320/1/FSKTM%202016%208%20UPM%20IR.pdf text en public doctoral Universiti Putra Malaysia Genetic algorithms Software measurement Sultan, Abu Bakar
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Sultan, Abu Bakar
topic	Genetic algorithms Software measurement
spellingShingle	Genetic algorithms Software measurement Bakar, Abubakar Diwani Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms
description	Software development life cycle maintenance has been advocated as the critical part that consumes more time and resources. To understand the magnitude of the task to maintain the software product, software metrics have been used to make quantification based on their respective software features. To predict software maintainace, the proper metrics need to be selected to avoid the duplication or the outlying of the potential metrics. This is because on one hand, the individual metrics deals with only a single feature of the object-oriented systems, while on the other hand; the suites either contain duplicate metrics of the same goal or lack some important metrics that match the common attributes in the software products. The latest effort to solve this selection problem is the development of the metrics selection model that uses genetic algorithm (GA). However, the process failed to state clearly the encoding strategy in its initial stage. This thesis clarifies the issue using the objective method to develop the GA metric selection model for predicting the maintainability of object-oriented systems. The study proposes the use of software metric thresholds in the classification process during the GA representation. The software metric thresholds were used as indication for identifying unsafe design in software engineering. To evaluate this technique, an experiment was conducted on two geospatial systems developed using Java programming language where the Chidamber and Kemerer (CK) metrics were used. The proposed technique was also compared to the ranking results from the experts. The comparison results obtained when compared with those of Principal Component Analysis and the complete software metric suite were very promising. Moreover, the three techniques show significant differences in both treatments when compared using analysis of variance.
format	Thesis
qualification_level	Doctorate
author	Bakar, Abubakar Diwani
author_facet	Bakar, Abubakar Diwani
author_sort	Bakar, Abubakar Diwani
title	Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms
title_short	Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms
title_full	Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms
title_fullStr	Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms
title_full_unstemmed	Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms
title_sort	software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/69320/1/FSKTM%202016%208%20UPM%20IR.pdf
_version_	1747812684996280320

Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms

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