Title
Multi-purpose, multi-level feature modeling of large-scale industrial software systems.
Abstract
Feature models are frequently used to capture the knowledge about configurable software systems and product lines. However, feature modeling of large-scale systems is challenging as models are needed for diverse purposes. For instance, feature models can be used to reflect the perspectives of product management, technical solution architecture, or product configuration. Furthermore, models are required at different levels of granularity. Although numerous approaches and tools are available, it remains hard to define the purpose, scope, and granularity of feature models. This paper first reports results and experiences of an exploratory case study on developing feature models for two large-scale industrial automation software systems. We report results on the characteristics and modularity of the feature models, including metrics about model dependencies. Based on the findings from the study, we developed FORCE, a modeling language, and tool environment that extends an existing feature modeling approach to support models for different purposes and at multiple levels, including mappings to the code base. We demonstrate the expressiveness and extensibility of our approach by applying it to the well-known Pick and Place Unit example and an injection molding subsystem of an industrial product line. We further show how our approach supports consistency between different feature models. Our results and experiences show that considering the purpose and level of features is useful for modeling large-scale systems and that modeling dependencies between feature models is essential for developing a system-wide perspective.
Year
DOI
Venue
2018
10.1007/s10270-016-0564-7
Software and System Modeling
Keywords
Field
DocType
Case study,Feature modeling,Large-scale software systems
Data mining,Systems engineering,Computer science,Solution architecture,Modeling language,Software system,Automation,Feature model,Product management,Granularity,Modularity
Journal
Volume
Issue
ISSN
17
3
1619-1374
Citations 
PageRank 
References 
2
0.36
52
Authors
8
Name
Order
Citations
PageRank
Daniela Rabiser131.05
Herbert Prähofer218917.00
Paul Grunbacher330625.59
Michael Petruzelka420.36
Klaus Eder580.80
Florian Angerer61076.78
Mario Kromoser720.36
Andreas Grimmer8667.44