Ebrahim Bagheri – Publication Page

Towards Automated Feature Model Configuration With Optimizing Non-Functional Requirements

Mohsen Asadi, Samaneh Soltani, Dragan Gasevic, Marek Hatala and Ebrahim Bagheri
Reference:
Mohsen Asadi, Samaneh Soltani, Dragan Gasevic, Marek Hatala and Ebrahim Bagheri Towards Automated Feature Model Configuration With Optimizing Non-Functional Requirements. In Information and Software Technology Journal, 56 (9): 1144–1165, 2014.
Links to Publication: [doi]
Abstract:
Context: A Software Product Line is a family of software systems that share some common features but also have signi cant variabilities. A feature model is a variability modeling artifact, which represents di erences among software products with respect to the variability relationships among their features. Having a feature model along with a reference model developed in the domain engineering lifecycle, a concrete product of the family is derived by binding the variation points in the feature model (called con figuration process) and by instantiating the reference model. Objective: In this work we address the feature model con guration problem and propose a framework to automatically select suitable features that satisfy both the functional and non-functional preferences and constraints of stakeholders. Additionally, interdependencies between various non-functional properties are taken into account in the framework. Method: The proposed framework combines Analytical Hierarchy Process (AHP) and Fuzzy Cognitive Maps (FCM) to compute the non-functional properties weights based on stakeholders' preferences and interdependencies between non-functional properties. Afterwards, Hierarchical Task Network (HTN) planning is applied to nd the optimal feature model con guration. Result: Our approach improves state-of-art of feature model con guration by considering positive or negative impacts of the features on non-functional properties, the stakeholders' preferences, and non-functional interdependencies. The approach presented in this paper extends earlier work presented in [1] from several distinct perspectives including mechanisms handling interdependencies between non-functional properties, proposing a novel tooling architecture, and o ering visualization and interaction techniques for representing functional and non-functional aspects of feature models. Conclusion: Our experiments show the scalability of our con guration approach when considering both functional and non-functional requirements of stakeholders.
Bibtex Entry:
@article{IST2014, author = {Mohsen Asadi, Samaneh Soltani, Dragan Gasevic, Marek Hatala and Ebrahim Bagheri}, title = {Towards Automated Feature Model Configuration With Optimizing Non-Functional Requirements}, journal = {Information and Software Technology Journal}, volume = {56}, number = {9}, pages = {1144–1165}, year = {2014}, ee = {http://www.sciencedirect.com/science/article/pii/S0950584914000640}, abstract = {Context: A Software Product Line is a family of software systems that share some common features but also have signi cant variabilities. A feature model is a variability modeling artifact, which represents di erences among software products with respect to the variability relationships among their features. Having a feature model along with a reference model developed in the domain engineering lifecycle, a concrete product of the family is derived by binding the variation points in the feature model (called con figuration process) and by instantiating the reference model. Objective: In this work we address the feature model con guration problem and propose a framework to automatically select suitable features that satisfy both the functional and non-functional preferences and constraints of stakeholders. Additionally, interdependencies between various non-functional properties are taken into account in the framework. Method: The proposed framework combines Analytical Hierarchy Process (AHP) and Fuzzy Cognitive Maps (FCM) to compute the non-functional properties weights based on stakeholders' preferences and interdependencies between non-functional properties. Afterwards, Hierarchical Task Network (HTN) planning is applied to nd the optimal feature model con guration. Result: Our approach improves state-of-art of feature model con guration by considering positive or negative impacts of the features on non-functional properties, the stakeholders' preferences, and non-functional interdependencies. The approach presented in this paper extends earlier work presented in [1] from several distinct perspectives including mechanisms handling interdependencies between non-functional properties, proposing a novel tooling architecture, and o ering visualization and interaction techniques for representing functional and non-functional aspects of feature models. Conclusion: Our experiments show the scalability of our con guration approach when considering both functional and non-functional requirements of stakeholders.} }




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