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25 February 1999 Knowledge discovery and validation in software metrics databases
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Miyoung Shin and Amrit L. Goel "Knowledge discovery and validation in software metrics databases", Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); https://doi.org/10.1117/12.339985
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