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Principles of Feature Modeling

Conference paper
Authors D. Nesic
J. Kruger
S. Stanciulescu
Thorsten Berger
Published in ESEC/FSE 2019. Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
ISBN 978-1-4503-5572-8
Publisher IEEE
Publication year 2019
Published at Department of Computer Science and Engineering (GU)
Language en
Keywords Feature models, modeling principles, software product lines, software, variability, management, framework, languages, systems
Subject categories Software Engineering


Feature models are arguably one of the most intuitive and successful notations for modeling the features of a variant-rich software system. Feature models help developers to keep an overall under-standing of the system, and also support scoping, planning, development, variant derivation, configuration, and maintenance activities that sustain the system's long-term success. Unfortunately, feature models are difficult to build and evolve. Features need to be identified, grouped, organized in a hierarchy, and mapped to software assets. Also, dependencies between features need to be declared. While feature models have been the subject of three decades of research, resulting in many feature-modeling notations together with automated analysis and configuration techniques, a generic set of principles for engineering feature models is still missing. It is not even clear whether feature models could be engineered using recurrent principles. Our work shows that such principles in fact exist. We analyzed feature-modeling practices elicited from ten interviews conducted with industrial practitioners and from 31 relevant papers. We synthesized a set of 34 principles covering eight different phases of feature modeling, from planning over model construction, to model maintenance and evolution. Grounded in empirical evidence, these principles provide practical, context-specific advice on how to perform feature modeling, describe what information sources to consider, and highlight common characteristics of feature models. We believe that our principles can support researchers and practitioners enhancing feature-modeling tooling, synthesis, and analyses techniques, as well as scope future research.

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