Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within
software product lines. The work presented identifies limitations and challenges within
the current feature modeling approaches. Those limitations include, but not limited to,
the dearth of satisfactory cognitive presentation, inconveniency in scalable systems,
inflexibility in adapting changes, nonexistence of predictability of models behavior, as
well as the lack of probabilistic quantification of model’s implications and decision
support for reasoning under uncertainty. The work in this thesis addresses these
challenges by proposing a series of solutions. The first solution is the construction of a
Bayesian Belief Feature Model, which is a novel modeling approach capable of
quantifying the uncertainty measures in model parameters by a means of incorporating
probabilistic modeling with a conventional modeling approach. The Bayesian Belief
feature model presents a new enhanced feature modeling approach in terms of truth
quantification and visual expressiveness. The second solution takes into consideration
the unclear support for the reasoning under the uncertainty process, and the challenging
constraint satisfaction problem in software product lines. This has been done through the
development of a mathematical reasoner, which was designed to satisfy the model
constraints by considering probability weight for all involved parameters and quantify
the actual implications of the problem constraints. The developed Uncertain Constraint
Satisfaction Problem approach has been tested and validated through a set of designated
experiments.
Profoundly stating, the main contributions of this thesis include the following:
• Develop a framework for probabilistic graphical modeling to build the purported
Bayesian belief feature model.
• Extend the model to enhance visual expressiveness throughout the integration of
colour degree variation; in which the colour varies with respect to the predefined
probabilistic weights.
• Enhance the constraints satisfaction problem by the uncertainty measuring of the
parameters truth assumption.
• Validate the developed approach against different experimental settings to
determine its functionality and performance.