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improved neural net-based approach for predicting software quality
- 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 |
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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. |
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Subject(s): |
Neural networks (Computer science) Computer software--Development Computer software--Quality control Software engineering |
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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. |