A process model defines the activities of a business process and their attributes (e.g. cost and time). Process models typically are instantiated several times and every process instance may be executed differently based on the context and the requirements of target stakeholders. Hence, several variants of the same process model may coexist in organizations which urges the organizations to support flexible processes in order to cope with process variabilities. Motivated by the need of flexible process models, a number of approaches have been proposed for the development of customizable process models which integrate variability in process models.The development and adaptation of customizable process models raise several challenges: 1) the need for taking into account several variability types (i.e. OR, alternative, and optional) which may occur in a customizable process model; 2) integrating variability into process models impose additional modeling complexity; 3) deriving a process variant from a customizable process model requires the close consideration of a target application requirements and relation between variants and requirements; 4) ensuring compliance of a process variant with behavioral and configuration constraints formulated in a customizable process model. This dissertation presents a feature oriented customization and validation framework for customizable process models. The customization component relies on software product lines and utilizes feature modeling techniques for modeling variability in customizable process models. Additionally, a pre-configuration process, a decision making technique called Stratified Analytical Hierarchy Process (S-AHP), and Artificial Intelligent Planning Techniques are provided to derive a process variant from a customizable process model based on the stakeholders requirements. The validation component identifies a set possible inconsistency patterns between requirements model (represented by goal model), variability model (represent by feature model), and customizable process models and employs Description Logic to detect the inconsistencies.We evaluated the framework using a set of experiments and explored the running time of the proposed techniques under different sizes of models and constraints. The results show that the running time of proposed techniques is tractable in practical customizable process models. Additionally, a comparative analysis of the components of the framework is conducted which reveals improvements over state of the art in customizable process models.
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