Abstract
Department of Software Engineering, In the current application development strategies, families of productsare developed with personalized configurations to increase stakeholders’ satisfaction. Product lines have theability to address several requirements due to their reusability and configuration properties. The structuringand prioritizing of configuration requirements facilitate the development processes, whereas it increases theconflicts and inadequacies. This increases human effort, reducing user satisfaction, and failing to accommodatea continuous evolution in configuration requirements. To address these challenges, we propose a framework formanaging the prioritization process considering heterogeneous stakeholders priority semantically. Featuresare analyzed, and mined configuration priority using the data mining method based on frequently accessed andchanged configurations. Firstly, priority is identified based on heterogeneous stakeholder’s perspectives usingthree factors functional, experiential, and expressive values. Secondly, the mined configuration is based on frequentlyaccessed or changed configuration frequency to identify the new priority for reducing failures or errorsamong configuration interaction. We evaluated the performance of the proposed framework with the help ofan 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 theproposed framework, for all selected cases.
Highlights
The software functional requirements play an important role in the development of component-based systems for agility and timely delivery of products, with high quality
RQ1 is analyzed for efficiency and the FRP is compared with conventional methods using questionnaire-based participant review
PG1 behaves as a treatment group (TG), PG2 behaves as a controlled group (CG), and vice versa
Summary
The software functional requirements play an important role in the development of component-based systems for agility and timely delivery of products, with high quality. To address this problem, researchers and industries have implemented and proposed different methods for prioritizations, with the integration of data mining, artificial intelligence, and machine learning techniques These still lack prioritization to handle larger sets of configurations due to reuse interfaces of different components for core assets and variant management in the dynamic and frequently changing environments, with lesser stakeholders’ participation and additional efforts. To resolve these issues, we adopted a data mining technique based on historical information for the active involvement of stakeholders, using their continuous feedback and reducing effort for the prioritization process.
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