Bioinformatics Approach to Data Mining of Software Bases

G. Stiglic, P. Kokol (Slovenia), and L. Lhotska (Czech Republic)

Keywords

Artificial intelligence, machine learning, multi-agent systems, software fault prediction.

Abstract

In this paper we propose a new method inspired by a multi-agent based system that was initially used for identification of significant genes in microarray databases. Gene subset selection is a common problem in the filed of bioinformatics. If we regard the software measurements values of a software module as a genome of that module, and the real world dynamic characteristic of that module as its phenotype (i. e. failures as a disease symptoms) we can borrow the established bioinformatics methods in the manner first to predict the module behaviour and second to data mine the relations between metrics and failures.

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