Business process families provide an over-arching representation of the possible business processes of a target domain. They are defined by capturing the similarities and differences among the possible business processes of the target domain. To realize a business process family into a concrete business process model, the variability points of the business process family need to be bounded. The decision on how to bind these variation points boils down to the stakeholders' requirements and needs. Given specific requirements from the stakeholders, the business process family can be configured. This paper formally introduces and empirically evaluates a framework called ConfBPFM that utilizes standard techniques for identifying stakeholders' quality requirements and employs a metaheuristic search algorithm (i.e., Genetic Algorithms) to optimally configure a business process family.

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Abstract. Quality evaluation is a challenging task in monolithic software systems, and is even more complex when it comes to Service-Oriented Software Product Lines (SOSPL), as it needs to analyze the attributes of a family of SOA systems. In SOSPL, variability can be managed and planned at the architectural level to develop a software product with the same set of functionalities but different degrees of non-functional quality attribute satisfaction. Therefore, architectural quality evaluation becomes crucial due to the fact that it allows for the examination of whether or not the final product satisfies and guarantees all the ranges of quality requirements within the envisioned scope. This paper addresses the open research problem of aggregating QoS attribute ranges with respect to architectural variability. Previous solutions for quality aggregation do not consider architectural variability for composite services. Our approach introduces variability patterns that can possibly occur at the architectural level of a SOSPL. We propose an aggregation model for QoS computation which takes both variability and composition patterns into account.

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Since the introduction in the early nineties, feature models receive a great deal of attention in industry and academia. Industrial success stories in applying feature models for modeling software product lines, and using them for configuring software-intensive systems motivate academia to discover ways to integrate different feature dependencies into the feature model, and automate verified feature configuration. In this paper we demonstrate how ontologies and Semantic Web technologies facilitate seamless integration of required external services and deployment platform capabilities into the feature model. Furthermore, we also contribute with an algorithm for automating staged configuration using Semantic Web reasoners to discover unfeasible features of the feature model.

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Feature Models encapsulate functionalities and quality properties of a product family. The employment of feature models for managing variability and commonality of large-scale product families raises an important question: on what basis should the features of a product family be selected for a target software application, which is going to be derived from the product family. Thus, the selection of the most suitable features for a specific application requires the understanding of its stakeholders' intentions and also the relationship between their intentions and the available software features. To address this important issue, we adopt a standard goal-oriented requirements engineering framework, i.e., the i* framework, for identifying stakeholders' intentions and propose an approach for explicitly mapping and bridging between the features of a product family and the goals and objectives of the stakeholders. We propose a novel approach to automatically preconfigure a given feature model based on the objectives of the target product stakeholders. Also, our approach is able to elucidate the rationale behind the selection of the most important features of a family for a target application.