The state of adoption and the challenges of systematic variability management in industry
Artikel i vetenskaplig tidskrift, 2020

© 2020, The Author(s). Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop portfolios of products—is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on re-engineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium- to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.

Variability management

Multiple-case study

Software product lines

Challenges

Författare

Thorsten Berger

Chalmers, Data- och informationsteknik, Software Engineering

Jan-Philipp Steghöfer

Chalmers, Data- och informationsteknik, Software Engineering

Tewfik Ziadi

Université Pierre et Marie Curie (UPMC)

Jacques Robin

Université Paris Pantheon-Sorbonne

Jabier Martinez

Tecnalia

Empirical Software Engineering

1382-3256 (ISSN) 1573-7616 (eISSN)

Vol. 25 3 1755-1797

Ämneskategorier (SSIF 2025)

Programvaruteknik

Datavetenskap (datalogi)

DOI

10.1007/s10664-019-09787-6

Mer information

Senast uppdaterat

2025-11-17