Reasoning about edits to feature models
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Selecting highly optimal architectural feature sets with Filtered Cartesian Flattening
Journal of Systems and Software
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
FaMa-OVM: a tool for the automated analysis of OVMs
Proceedings of the 16th International Software Product Line Conference - Volume 2
Strategies for testing products in software product lines
ACM SIGSOFT Software Engineering Notes
Reverse engineering feature models with evolutionary algorithms: an exploratory study
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Towards automated testing and fixing of re-engineered feature models
Proceedings of the 2013 International Conference on Software Engineering
SPLEMMA: a generic framework for controlled-evolution of software product lines
Proceedings of the 17th International Software Product Line Conference co-located workshops
Automated generation of computationally hard feature models using evolutionary algorithms
Expert Systems with Applications: An International Journal
An Ontological Rule-Based Approach for Analyzing Dead and False Optional Features in Feature Models
Electronic Notes in Theoretical Computer Science (ENTCS)
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The automated analysis of feature models is a flourishing research topic that has called the attention of both researchers and practitioners during the last two decades. During this time, the number of tools and techniques enabling the analysis of feature models has increased and also their complexity. In this scenario, the lack of specific testing mechanisms to assess the correctness and good performance of analysis tools is becoming a major obstacle hindering the development of tools and affecting their quality and reliability. In this paper, we present BeTTy, a framework for BEnchmarking and TesTing on the analYsis of feature models. Among other features, BeTTy enables the automated detection of faults in feature model analysis tools. Also, it supports the generation of motivating test data to evaluate the performance of analysis tools in both average and pessimistic cases. Part of the functionality of the framework is provided through a web-based interface facilitating the random generation of both classic and attributed feature models.