Towards Mining Variable Features in Software Product Lines during Development

Abstract

Software Product Line (SPL) engineering provides a strong vision to develop highly adaptable software systems using the common characteristics as well as controlling the variabilities of a product line of products. The specification of some variable features and their exact reuse is, however, a major problem, in particular when handling heterogeneous families of products and concurring multi-facet demands of the stakeholders. It is complicated by the fact that approaches to managing commonality and variability are not systematic in the early phases of requirement specification. The current feature selection algorithms tend not to be rigorous enough to deal with multi-stakeholder viewpoints and fully categorize features by their natural facets, which makes them inefficient and impairs their reusability. To constrain this serious issue of research, a new, articulate model of variable feature mining and selection in SPLs is given in this paper. The distinctive points of our methodology are that we combine systematic requirements collecting, rigorous data preprocessing, and an unusual aspect-based feature clustering strategy based on using unsupervised learning algorithms (We are using Weka, for instance). This feature-conscious classification with subsequent learning through supervision to identify two types of features, Common and Variable ones, distinguishes our model over the traditional, simpler divide-and-conquer methods due to a more accurate and context-situated feature taxonomy. Two industrial studies of a biometric system and an online auction system were conducted in a rigorous manner to assess the proposed model. Early findings have shown that the model suggested is of critical importance in improving the procedure of variable feature selection. Major discoveries showed significant advances in the effective accuracy of classification features, significant advancements in the efficiency of the feature selection (e.g., less time and effort than manual processes involved), and raised the satisfaction levels of the stakeholders with the chosen feature sets. The study helps practitioners in the industry because it provides them with a data-driven, structured approach that enhances the feature selection task, in addition to the fact that the study helps deal with reusability and customization across the SPL development life-cycle successfully. Finally, this model, combined with the earlier elements, helps to arrive at a solution that is time-to-market quicker and also yields product configurations that are stronger and meet a fundamental need of existing SPL practices.