Recovering Architectural Variability of a Family of Product Variants

TL;DR

This paper presents a formal approach using Formal Concept Analysis to reverse engineer and identify architectural variability and dependencies in product family variants, addressing a gap in existing variability management at the architectural level.

Contribution

It introduces a novel FCA-based method for extracting architectural variability from product variants, focusing on architectural elements rather than requirements.

Findings

Successfully identified architectural variability in open-source product families

Revealed dependencies among architectural elements

Validated approach on Mobile Media and Health Watcher families

Abstract

A Software Product Line (SPL) aims at applying a pre-planned systematic reuse of large-grained software artifacts to increase the software productivity and reduce the development cost. The idea of SPL is to analyze the business domain of a family of products to identify the common and the variable parts between the products. However, it is common for companies to develop, in an ad-hoc manner (e.g. clone and own), a set of products that share common functionalities and differ in terms of others. Thus, many recent research contributions are proposed to re-engineer existing product variants to a SPL. Nevertheless, these contributions are mostly focused on managing the variability at the requirement level. Very few contributions address the variability at the architectural level despite its major importance. Starting from this observation, we propose, in this paper, an approach to reverse engineer the architecture of a set of product variants. Our goal is to identify the variability and dependencies among architectural-element variants at the architectural level. Our work relies on Formal Concept Analysis (FCA) to analyze the variability. To validate the proposed approach, we experimented on two families of open-source product variants; Mobile Media and Health Watcher. The results show that our approach is able to identify the architectural variability and the dependencies.