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Proceedings Paper

Knowledge discovery and validation in software metrics databases
Author(s): Miyoung Shin; Amrit L. Goel
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Paper Abstract

The explosive growth of commercial and scientific databases has outpaced our ability to manually analyze and interpret this data. The newly emerging interdisciplinary field of knowledge discovery in databases (KDD), provides methodologies for seeking valuable and useful information from these databases. In this paper, we describe a methodology for identifying high fault modules in software metrics databases. It employs radial basis function model for the data mining phase of the KDD process based on our newly developed algorithm. We use the well-known bootstrap method for model validation and accuracy estimation of the classification task. As an example, a genuine problem from NASA software database is explored.

Paper Details

Date Published: 25 February 1999
PDF: 8 pages
Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339985
Show Author Affiliations
Miyoung Shin, Syracuse Univ. (United States)
Amrit L. Goel, Syracuse Univ. (United States)

Published in SPIE Proceedings Vol. 3695:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology
Belur V. Dasarathy, Editor(s)

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