A Data Mining Technique to Improve Configuration Prioritization Framework for Component-Based Systems: An Empirical Study

Abstract

Department of Software Engineering, In the current application development strategies, families of products
are developed with personalized configurations to increase stakeholders’ satisfaction. Product lines have the
ability to address several requirements due to their reusability and configuration properties. The structuring
and prioritizing of configuration requirements facilitate the development processes, whereas it increases the
conflicts and inadequacies. This increases human effort, reducing user satisfaction, and failing to accommodate
a continuous evolution in configuration requirements. To address these challenges, we propose a framework for
managing the prioritization process considering heterogeneous stakeholders priority semantically. Features
are analyzed, and mined configuration priority using the data mining method based on frequently accessed and
changed configurations. Firstly, priority is identified based on heterogeneous stakeholder’s perspectives using
three factors functional, experiential, and expressive values. Secondly, the mined configuration is based on frequently
accessed or changed configuration frequency to identify the new priority for reducing failures or errors
among configuration interaction. We evaluated the performance of the proposed framework with the help of
an experimental study and by comparing it with analytical hierarchical prioritization (AHP) and Clustering.
The results indicate a significant increase (more than 90 percent) in the precision and the recall value of the
proposed framework, for all selected cases.