DMCA
Evaluating Predictive Quality Models Derived from Software Measures: Lessons Learned (1997)
Cached
Download Links
- [www.cs.umd.edu]
- [iese.iese.fhg.de]
- [www.iese.fhg.de]
- DBLP
Other Repositories/Bibliography
| Citations: | 31 - 0 self |
BibTeX
@MISC{Lanubile97evaluatingpredictive,
author = {Filippo Lanubile},
title = {Evaluating Predictive Quality Models Derived from Software Measures: Lessons Learned},
year = {1997}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
1 This paper describes an empirical comparison of several modeling techniques for predicting the quality of software components early in the software life cycle. Using software product measures, we built models that classify components as high-risk, i.e., likely to contain faults, or low-risk, i.e., likely to be free of faults. The modeling techniques evaluated in this study include principal component analysis, discriminant analysis, logistic regression, logical classification models, layered neural networks, and holographic networks. These techniques provide a good coverage of the main problemsolving paradigms: statistical analysis, machine learning, and neural networks. Using the results of independent testing, we determined the absolute worth of the predictive models and compare their performance in terms of misclassification errors, achieved quality, and verification cost. Data came from 27 software systems, developed and tested during three years of project-intensive academic cou...
Keyphrases
software measure predictive quality model derived neural network statistical analysis principal component analysis discriminant analysis misclassification error predictive model main problemsolving paradigm absolute worth holographic network software system software product measure good coverage independent testing empirical comparison software life cycle software component machine learning classify component logistic regression logical classification model verification cost project-intensive academic cou
