Analysis of Features using Feature Model in Software Product Line: A Case Study

Abstract

This paper shows an analysis of features of email system using feature model in a Software Product Line (SPL). The core features that can be used by different SPLs are identified using feature model. The analysis is based on two primary measures -reusability and consistency. Reusability measures the level of frequency of usage of the feature in developing a new software product line and consistency ensures that the core features are consistent in a software product line. On the basis of reusability measure, the core features are classified into four different categories. These measures help in understanding the Return on Investment in a software product line.

Feature selection optimisation of software product line using metaheuristic techniques

“…In Ding et al (2011) they have classified features in terms of high to low reusability level (Core Type I, II, III, and IV) (Ding et al, 2011;Yadav and Kumari, 2018). If the reusability value is greater than 0.5 it comes in high reusable category also called as core type I.…”

“…If the reusability of feature lies in 0.05 to 0.2 then it is called as core type feature III and if the value is less than 0.05 then it called as core type feature IV. Yadav et al (2018) have considered a case study and identified features which are high reusable and least reusable. Authors have done in-depth analysis of feature model.…”

An Industrial Case Study for Adopting Software Product Lines in Automotive Industry

“…Close to our work, the authors in [23] introduce a systematic reuse method called Variation Point Method (VPM), which models variability in a process that starts with common requirements. Important research works related to managing variability are [4,8,[24][25][26][27][28]. However, not only is our approach designed to consider the variants that are related to the same family in the automotive domain but also it is used to address commonality and variability of variants family at a higher level of abstraction using variability model (i.e., an FM).…”