THE APPLICATION OF ROUGH SETS THEORY AS A TOOL FOR ANALYZING DYNAMICALLY COLLECTED DATA

A new generation of techniques and tools is emerging to intelligently assist humans in analyzing mountains of data, finding useful knowledge and in some cases, performing analysis automatically. Rough Sets Theory RST is a powerful mathematical tool to handle vagueness and uncertainty inherent in making decisions. The major application areas include: business failure prediction, database marketing, financial investment, and biomedical data mining. In this paper, the usefulness of RST in the context of software engineering is investigated. Different scenarios are used to exercise a certain feature and the computational methods involved during execution are collected. As known for all dynamic-based approaches, this dynamically collected data is sometimes imprecise or incomplete. Thus, an RST approach is very suitable for representing and analyzing such data. The approach is tested on the large-scale open source web browser MOZILLA. The results show that the RST based approach is more efficient in comparison with a similar approach based on formal concept analysis.