A data mining technique to improve configuration prioritization framework for component-based systems: An empirical study

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 results in increasing 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 prioritiza-tion 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 function-al, 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 indi-cate a significant increase (more than 90 percent) in the precision and the recall value of the proposed frame-work, for all selected cases.