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improved neural net-based approach for predicting software quality

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Date Issued:
1995
Summary:
Accurately predicting the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take action against emerging quality problems. Most often the predictive models are based upon multiple regression analysis which become unstable when certain data assumptions are not met. Since neural networks require no data assumptions, they are more appropriate for predicting software quality. This study proposes an improved neural network architecture that significantly outperforms multiple regression and other neural network attempts at modeling software quality. This is demonstrated by applying this approach to several large commercial software systems. After developing neural network models, we develop regression models on the same data. We find that the neural network models surpass the regression models in terms of predictive quality on the data sets considered.
Title: An improved neural net-based approach for predicting software quality.
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Name(s): Guasti, Peter John.
Florida Atlantic University, Degree grantor
Khoshgoftaar, Taghi M., Thesis advisor
Pandya, Abhijit S., Thesis advisor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1995
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 122 p.
Language(s): English
Summary: Accurately predicting the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take action against emerging quality problems. Most often the predictive models are based upon multiple regression analysis which become unstable when certain data assumptions are not met. Since neural networks require no data assumptions, they are more appropriate for predicting software quality. This study proposes an improved neural network architecture that significantly outperforms multiple regression and other neural network attempts at modeling software quality. This is demonstrated by applying this approach to several large commercial software systems. After developing neural network models, we develop regression models on the same data. We find that the neural network models surpass the regression models in terms of predictive quality on the data sets considered.
Identifier: 15134 (digitool), FADT15134 (IID), fau:11909 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.C.S.)--Florida Atlantic University, 1995.
Subject(s): Neural networks (Computer science)
Computer software--Development
Computer software--Quality control
Software engineering
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/15134
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.