Orthogonal variability modeling to support multi-cloud application configuration

Cloud service providers benefit from a vast majority of customers due to variability and making profit from commonalities between the cloud services that they provide. Recently, application configuration dimensions has been increased dramatically due to multi-tenant, multi-device and multi-cloud paradigm. This challenges the configuration and customization of cloud-based software that are typically offered as a service due to the intrinsic variability. In this paper, we present a model-driven approach based on variability models originating from the software product line community to handle such multi-dimensional variability in the cloud. We exploit orthogonal variability models to systematically manage and create tenant-specific configuration and customizations. We also demonstrate how such variability models can be utilized to take into account the already deployed application parts to enable harmonized deployments for new tenants in a multi-cloud setting. The approach considers application functional and non-functional requirements to provide a set of valid multi-cloud configurations. We illustrate our approach through a case study

Sensor Data Visualisation: A Composition-Based Approach to Support Domain Variability

International audienceIn the context of the Internet of Things, sensors are surrounding our environment. These small pieces of electronics are inserted in everyday life's elements (e.g., cars, doors, radiators, smartphones) and continuously collect information about their environment. One of the biggest challenges is to support the development of accurate monitoring dashboard to visualise such data. The one-size-fits-all paradigm does not apply in this context, as user's roles are variable and impact the way data should be visualised: a building manager does not need to work on the same data as classical users. This paper presents an approach based on model composition techniques to support the development of such monitoring dashboards, taking into account the domain variability. This variability is supported at both implementation and modelling levels. The results are validated on a case study named SmartCampus, involving sensors deployed in a real academic campus

A Case Study Evaluation of the Guideline-Supported QUPER Model for Elicitation of Quality Requirements

[Context & motivation] For market-driven software product developing organizations operating on a competitive open market, it is important to plan the product’s releases so that they can reach the market as early as possible with a competitive level of quality compared to its competitors' products. Hence, quality requirements can be seen as a key competitive advantage. The QUPER model was developed with the aim to support high-level decision-making in release planning of quality requirements. [Question/problem] As a follow up on previous studies on QUPER, this study investigates: What are practitioner's views on the utilities of QUPER extended with guidelines including domain-specific examples? [Principal ideas/results] In the presented case study, a set of detailed guidelines of how to apply QUPER in practice, including how to handle cost dependencies between quality requirements, was evaluated at a case company in the mobile handset domain with 24 professionals using real quality requirements. [Contribution] The results point to the importance of having concrete guidelines combined with instructive examples from real practice, while it is not always obvious for a practitioner to transfer cost-dependency examples into the domains that are different from the example domain. The transferability of guidelines and examples to support methodology adoption is an interesting issue for further research

Carrying Ideas from Knowledge-Based Configuration to Software Product Lines

Software variability modelling (SVM) has become a central concern in software product lines -- especially configurable software product lines (CSPL) require rigorous SVM. Dynamic SPLs, service oriented SPLs, and autonomous or pervasive systems are examples where CSPLs are applied. Knowledge-based configuration (KBC) is an established way to address variability modelling aiming for the automatic product configuration of physical products. Our aim was to study what major ideas from KBC can be applied to SVM, particularly in the context of CSPLs. Our main contribution is the identification of major ideas from KBC that could be applied to SVM. First, we call for the separation of types and instances. Second, conceptual clarity of modelling concepts, e.g., having both taxonomical and compositional relations would be useful. Third, we argue for the importance of a conceptual basis that provides a foundation for multiple representations, e.g., graphical and textual. Applying the insights and experiences embedded in these ideas may help in the development of modelling support for software product lines, particularly in terms of conceptual clarity and as a basis for tool support with a high level of automation.Peer reviewe