Using First-Order Logic for Product Line Model Validation
SPLC 2 Proceedings of the Second International Conference on Software Product Lines
Feature-driven requirement dependency analysis and high-level software design
Requirements Engineering
A BDD-Based Approach to Verifying Clone-Enabled Feature Models' Constraints and Customization
ICSR '08 Proceedings of the 10th international conference on Software Reuse: High Confidence Software Reuse in Large Systems
Automated Diagnosis of Product-Line Configuration Errors in Feature Models
SPLC '08 Proceedings of the 2008 12th International Software Product Line Conference
Feature models, grammars, and propositional formulas
SPLC'05 Proceedings of the 9th international conference on Software Product Lines
Using constraint programming to verify DOPLER variability models
Proceedings of the 5th Workshop on Variability Modeling of Software-Intensive Systems
Binary-search based verification of feature models
ICSR'11 Proceedings of the 12th international conference on Top productivity through software reuse
Towards detecting redundancy in domain engineering process using first order logic rules
International Journal of Knowledge Engineering and Soft Data Paradigms
Automated generation of computationally hard feature models using evolutionary algorithms
Expert Systems with Applications: An International Journal
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Feature models provide an effective approach to requirements reuse. One important problem related to feature models is the verification problem, which is NP-complete in theory. The existing approaches to feature models' verification mostly focus on how to automate the verification of feature models using third-party's tools, while these tools are usually designed to resolve general kinds of problems. However, by simply using these third-party's tools, large-sized feature models still can hardly be verified within acceptable time. We argue that, to improve the efficiency of verification, the problem itself should be at first optimized. In this paper, we propose an optimization strategy to feature models' verification, in which, verification-irrelevant features and constraints are eliminated from feature models and the problem size of verification is therefore reduced. We prove the correctness of this strategy, while experiments show the effectiveness of this strategy.