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
Automated error analysis for the agilization of feature modeling
Journal of Systems and Software
ICSR '09 Proceedings of the 11th International Conference on Software Reuse: Formal Foundations of Reuse and Domain Engineering
Feature models, grammars, and propositional formulas
SPLC'05 Proceedings of the 9th international conference on Software Product Lines
From feature models to decision models and back again an analysis based on formal transformations
Proceedings of the 16th International Software Product Line Conference - Volume 1
Test-Case design by feature trees
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
A formal approach for run-time verification of web applications using scope-extended LTL
Information and Software Technology
| Hi-index | 0.00 |
The purpose of feature models' verification is to detect deficiencies in feature models, so as to avoid the transmission of these deficiencies into subsequent core-asset and product development activities. Although many researchers have observed that the verification problem of feature models can be transformed into SAT problems and proposed to resolve this problem based on third-party's SAT-solver or model-checker tools, few of them point out how to use these third-party tools efficiently. In this paper, we present a binary-search based approach to feature models' verification. Our motivation is to decrease the number of times a SAT-solver is invoked during the verification of a feature model, and thus improve the verification efficiency. The basic idea is to change feature models' verification from the linear-search based approach to a binary-search approach, and thereby decrease the number of times to invoke a SAT-solver. Preliminary experiments show that as the number of levels in feature models increases, our approach manifests a better scalability than the linear-search based approach. This approach can be easily integrated into any feature modeling environment as its verification component.