Paper Info
Title
Using relation graphs for improved understanding of feature models in software product lines: [industry]
Abstract
Feature models are widely used for describing the variability of a software product line. A feature model contains a tree of features and a set of constraints over these features, which define valid feature combinations. In the industrial practice, large feature models containing hundreds of features and constraints are common. Furthermore, in a hierarchical product line a feature model can be related to other feature models through inter-model constraints. Due to the model size and complexity, understanding industrial feature models is a challenging task. In this paper, we describe the feature model understanding challenges reported by feature model developers at Robert Bosch GmbH. To support the developers in model understanding, we extend the idea of a feature implication graph to feature relation graph by abstracting groups of implications to feature relations. A transitively closed relation graph shows all modeled and implicit feature relations and spans all related feature models. The graph is also used to identify modeling problems, such as false optional or dead features, and to show the derivation of any implicit relation or problem from the modeled constraints. In a case study at Bosch, we evaluate the use of feature relation graph for model understanding. We propose further use cases of the graph, supporting model maintenance, evolution and configuration.
Year
DOI
Venue
2019
10.1145/3336294.3336317
Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A
Keywords
Field
DocType
feature model, implication graph, model understanding
Graph,Computer science,Theoretical computer science,Software
Conference
ISBN
Citations 
PageRank 
978-1-4503-7138-4
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Slawomir Duszynski141.11
Saura Jyoti Dhar220.73
Tobias Beichter320.73