2 research outputs found
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 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