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Complexity-based classification of software modules


Complexity-based classification of software modules

Wang, Jian Han (2008) Complexity-based classification of software modules. Masters thesis, Concordia University.

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Software plays a major role in many organizations. Organizational success depends partially on the quality of software used. In recent years, many researchers have recognized that statistical classification techniques are well-suited to develop software quality prediction models. Different statistical software quality models, using complexity metrics as early indicators of software quality, have been proposed in the past. At a high-level the problem of software categorization is to classify software modules into fault prone and non-fault prone. The focus of this thesis is two-fold. One is to study some selected classification techniques including unsupervised and supervised learning algorithms widely used for software categorization. The second emphasis is to explore a new unsupervised learning model, employing Bayesian and deterministic approaches. Besides, we evaluate and compare experimentally these approaches using a real data set. Our experimental results show that different algorithms lead to different statistically significant results.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Wang, Jian Han
Pagination:viii, 58 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Institute for Information Systems Engineering
Thesis Supervisor(s):Bouguila, Nizar
Identification Number:LE 3 C66I54M 2008 W365
ID Code:975929
Deposited By: Concordia University Library
Deposited On:22 Jan 2013 16:17
Last Modified:13 Jul 2020 20:09
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