Generative programming: methods, tools, and applications
Generative programming: methods, tools, and applications
Easing the Transition to Software Mass Customization
PFE '01 Revised Papers from the 4th International Workshop on Software Product-Family Engineering
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Software Product Line Engineering: Foundations, Principles and Techniques
Software Product Line Engineering: Foundations, Principles and Techniques
Software Product Lines in Action: The Best Industrial Practice in Product Line Engineering
Software Product Lines in Action: The Best Industrial Practice in Product Line Engineering
Feature Diagrams and Logics: There and Back Again
SPLC '07 Proceedings of the 11th International Software Product Line Conference
Reasoning about edits to feature models
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Proceedings of the 13th International Software Product Line Conference
Automated analysis of feature models 20 years later: A literature review
Information Systems
Automated metamorphic testing on the analyses of feature models
Information and Software Technology
Reverse engineering feature models
Proceedings of the 33rd International Conference on Software Engineering
Assessing the maintainability of software product line feature models using structural metrics
Software Quality Control
Reverse engineering architectural feature models
ECSA'11 Proceedings of the 5th European conference on Software architecture
Reverse Engineering Feature Models from Programs' Feature Sets
WCRE '11 Proceedings of the 2011 18th Working Conference on Reverse Engineering
Variability Modeling of Software-intensive Systems
On extracting feature models from product descriptions
Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems
BeTTy: benchmarking and testing on the automated analysis of feature models
Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems
Towards fixing inconsistencies in models with variability
Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems
On extracting feature models from sets of valid feature combinations
FASE'13 Proceedings of the 16th international conference on Fundamental Approaches to Software Engineering
Towards automated testing and fixing of re-engineered feature models
Proceedings of the 2013 International Conference on Software Engineering
Automated generation of computationally hard feature models using evolutionary algorithms
Expert Systems with Applications: An International Journal
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Successful software evolves, more and more commonly, from a single system to a set of system variants tailored to meet the similiar and yet different functionality required by the distinct clients and users. Software Product Line Engineering (SPLE) is a software development paradigm that has proven effective for coping with this scenario. At the core of SPLE is variability modeling which employs Feature Models (FMs) as the de facto standard to represent the combinations of features that distinguish the systems variants. Reverse engineering FMs consist in constructing a feature model from a set of products descriptions. This research area is becoming increasingly active within the SPLE community, where the problem has been addressed with different perspectives and approaches ranging from analysis of configuration scripts, use of propositional logic or natural language techniques, to ad hoc algorithms. In this paper, we explore the feasibility of using Evolutionary Algorithms (EAs) to synthesize FMs from the feature sets that describe the system variants. We analyzed 59 representative case studies of different characteristics and complexity. Our exploratory study found that FMs that denote proper supersets of the desired feature sets can be obtained with a small number of generations. However, reducing the differences between these two sets with an effective and scalable fitness function remains an open question. We believe that this work is a first step towards leveraging the extensive wealth of Search-Based Software Engineering techniques to address this and other variability management challenges.