Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation

Abstract

The large variety of computerized solutions (software and information systems) calls for a systematic approach to their comparison and evaluation. Different methods have been proposed over the years for analyzing the similarity and variability of systems. These methods get artifacts, such as requirements, design models, or code, of different systems (commonly in the same domain), identify and calculate their similarities, and represent the variability in models, such as feature diagrams. Most methods rely on implementation considerations of the input systems and generate outcomes based on predefined, fixed strategies of comparison (referred to as variability views). In this paper, we introduce an approach for mining relevant views for comparison and evaluation, based on the input artifacts. Particularly, we equip SOVA – a Semantic and Ontological Variability Analysis method – with data mining techniques in order to identify relevant views that highlight variability or similarity of the input artifacts (natural language requirement documents). The comparison is done using entropy and Rand index measures. The method and its outcomes are evaluated on a case of three photo sharing applications.