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Modeling software quality with classification trees using principal components analysis

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Date Issued:
1999
Summary:
Software quality models often have raw software metrics as the input data for predicting quality. Raw metrics are usually highly correlated with one another and thus may result in unstable models. Principal components analysis is a statistical method to improve model stability. This thesis presents a series of studies on a very large legacy telecommunication system. The system has significantly more than ten million lines of code written in a high level language similar to Pascal. Software quality models were developed to predict the class of each module either as fault-prone or as not fault-prone. We found out that the models based on principal components analysis were more robust than those based on raw metrics. We also found out that software process metrics can significantly improve the predictive accuracy of software quality models.
Title: Modeling software quality with classification trees using principal components analysis.
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Name(s): Shan, Ruqun.
Florida Atlantic University, Degree grantor
Khoshgoftaar, Taghi M., Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1999
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 80 p.
Language(s): English
Summary: Software quality models often have raw software metrics as the input data for predicting quality. Raw metrics are usually highly correlated with one another and thus may result in unstable models. Principal components analysis is a statistical method to improve model stability. This thesis presents a series of studies on a very large legacy telecommunication system. The system has significantly more than ten million lines of code written in a high level language similar to Pascal. Software quality models were developed to predict the class of each module either as fault-prone or as not fault-prone. We found out that the models based on principal components analysis were more robust than those based on raw metrics. We also found out that software process metrics can significantly improve the predictive accuracy of software quality models.
Identifier: 9780599537088 (isbn), 15714 (digitool), FADT15714 (IID), fau:12470 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 1999.
Subject(s): Principal components analysis
Computer software--Quality control
Software engineering
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/15714
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.