Sensitivity Analysis of Evolutionary Algorithm for Software Reusability

Abstract

Fast and competitive software industry demands rapid development using Component Based Software Development (CBSD). CBSD is dependent on the availability of the high-quality reusable component libraries. Recently, evolutionary multi-objective optimization algorithms have been used to identify sets of reusable software components from the source-code of Object Oriented (OO) software, using different quality indicators (e.g. cohesion, coupling, etc.). Sometimes, these used quality indicators are quite sensitive towards the small variations in their values, although they should not be. Therefore, this paper analyzes the sensitivity of the evolutionary technique for three quality indicators used during the identification: Frequent Usage Pattern (FUP), Semantic and evolutionary coupling. The sensitivity analysis is performed on three widely used open-source OO software. The experimentation is performed by mutating the system to different degrees. Results of the empirical analysis indicate that the semantic parameter is most sensitive and important. Ignoring this feature highly degrades the quality; FUP relation is uniformly sensitive and evolutionary relations's sensitivity is non-uniform.

Extraction and Evaluation of Software Components from Object-Oriented Artifacts

“…It consists of five main steps as depicted in [ 1,7,8]. Based on the study, it was determined that combined use of structural, conceptual, and changehistory based (called evolutionary) relations helps in estimating optimal dependency relations with 60% or more weight factor value assigned to evolutionary relations [2,9]. Further, the authors determined that frequent usage patterns help in measuring more accurate structural dependency relations [3].…”