Moving Faster Than Light
Moving Faster Than Light
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Date
1998-10-15
Authors
Lanubile, Filippo
Visaggio, Giuseppe
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Abstract
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 problem-solving 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 courses. A surprising result is that no model was able to
effectively discriminate between components with faults and
components without faults.
(Also cross-referenced as UMIACS-TR-96-14)