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Classification of software quality using Bayesian belief networks

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Date Issued:
2002
Summary:
In today's competitive environment for software products, quality has become an increasingly important asset to software development organizations. Software quality models are tools for focusing efforts to find faults early in the development. Delaying corrections can lead to higher costs. In this research, the classification Bayesian Networks modelling technique was used to predict the software quality by classifying program modules either as fault-prone or not fault-prone. A general classification rule was applied to yield classification Bayesian Belief Network models. Six classification Bayesian Belief Network models were developed based on quality metrics data records of two very large window application systems. The fit data set was used to build the model and the test data set was used to evaluate the model. The first two models used median based data cluster technique, the second two models used median as critical value to cluster metrics using Generalized Boolean Discriminant Function and the third two models used Kolniogorov-Smirnov test to select the critical value to cluster metrics using Generalized Boolean Discriminant Function; All six models used the product metrics (FAULT or CDCHURN) as predictors.
Title: Classification of software quality using Bayesian belief networks.
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Name(s): Dong, Yuhong.
Florida Atlantic University, Degree grantor
Khoshgoftaar, Taghi M., Thesis advisor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 2002
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 73 p.
Language(s): English
Summary: In today's competitive environment for software products, quality has become an increasingly important asset to software development organizations. Software quality models are tools for focusing efforts to find faults early in the development. Delaying corrections can lead to higher costs. In this research, the classification Bayesian Networks modelling technique was used to predict the software quality by classifying program modules either as fault-prone or not fault-prone. A general classification rule was applied to yield classification Bayesian Belief Network models. Six classification Bayesian Belief Network models were developed based on quality metrics data records of two very large window application systems. The fit data set was used to build the model and the test data set was used to evaluate the model. The first two models used median based data cluster technique, the second two models used median as critical value to cluster metrics using Generalized Boolean Discriminant Function and the third two models used Kolniogorov-Smirnov test to select the critical value to cluster metrics using Generalized Boolean Discriminant Function; All six models used the product metrics (FAULT or CDCHURN) as predictors.
Identifier: 9780493772745 (isbn), 12918 (digitool), FADT12918 (IID), fau:9791 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 2002.
Subject(s): Computer software--Quality control
Software measurement
Bayesian statistical decision theory
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/12918
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.