Towards automated feature model configuration with optimizing the non-functional requirements

Author: 
File(s): 
Date created: 
2012-07-06
Identifier: 
etd7368
Supervisor(s): 
Marek Hatala
Department: 
Communication, Art & Technology: School of Interactive Arts and Technology
Keywords: 
Software product line
Feature model
Configuration process
Planning techniques
Abstract: 

A Software Product Line is a family of software systems in a domain, which share some common features but also have significant variabilities. A feature model is a variability modeling artifact, which represents differences 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 configuration process) and by instantiating the reference model. However, feature model configuration is a cumbersome task because of: 1) the large number of features in industrial feature models, which increases the complexity of the configuration process; 2) the positive or negative impact of the features on non-functional properties; and 3) the stakeholders’ preferences with respect to the desirable non-functional properties of the final product. Several configuration techniques have already been proposed to facilitate automated product derivation. However, most of the current proposals are not designed to consider stakeholders’ preferences and constraints especially with regard to non-functional properties. In this work we address the feature model configuration problem and propose a framework, which employs an artificial intelligence planning technique to automatically select suitable features that satisfy both the functional and non-functional preferences and constraints of stakeholders. We also provide tooling support to facilitate the use of our framework. Our experiments show that despite the complexity involved in the simultaneous consideration of both functional and non-functional properties, our configuration technique is scalable.

Thesis type: 
(Thesis) M.Sc.
Document type: 
Thesis
Rights: 
Copyright remains with the author. The author granted permission for the file to be printed and for the text to be copied and pasted.
Statistics: