Qos Based Optimization Of Runtime Management Of Sensing Cloud Applications

IoT applications perceive and interact with the environment via smart devices and cloud services. When operating such applications one is faced with the challenge of configuring the smart devices and the cloud services in a manner, which achieves a high data quality at low operational costs. This work supports IoT operators with IoT collection strategies and cost optimization functions for data qualities, which are influenced by the interplay of smart device and cloud service configurations.

In this work, the authors analysed the co-dependency between models and analyses, particularly the structure and interdependence of artefacts and the feature-based decomposition and composition of model-based analyses. Their goal is to improve the maintainability of model-based analyses. They have investigated the co-dependency of Domain-specific Modelling Languages (DSMLs) and model-based analyses regarding evolvability, understandability, and reusability.

Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability.

Developing variable systems faces many challenges. Dependencies between interrelated artifacts within a product variant, such as code or diagrams, across product variants and across their revisions quickly lead to inconsistencies during evolution. This work provides a unification of common concepts and operations for variability management, identifies variability-related inconsistencies and presents an approach for view-based consistency preservation of variable systems.

This cumulative habilitation thesis, proposes concepts for (i) modelling and analysing dependability based on architectural models of software-intensive systems early in development, (ii) decomposition and composition of modelling languages and analysis techniques to enable more flexibility in evolution, and (iii) bridging the divergent levels of abstraction between data of the operation phase, architectural models and source code of the development phase.

Software vendors must consider confidentiality especially while creating software architectures because decisions made here are hard to change later. Our approach represents and analyzes data flows in software architectures. Systems specify data flows and confidentiality requirements specify limitations of data flows. Software architects use detected violations of these limitations to improve the system. We demonstrate how to integrate our approach into existing development processes.

Business processes and information systems evolve constantly and affect each other in non-trivial ways. Aligning security requirements between both is a challenging task. This work presents an automated approach to extract access control requirements from business processes with the purpose of transforming them into a) access permissions for role-based access control and b) architectural data flow constraints to identify violations of access control in enterprise application architectures.

This book constitutes revised selected papers of the Second International Workshop on Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, DEVOPS 2019, held at the Château de Villebrumier, France, in May 2019. The 15 papers presented in this volume were carefully reviewed and selected from 19 submissions. They cover a wide range of problems arising from DevOps and related approaches: current tools, rapid development-deployment processes, modeling frameworks, anomaly detection in software releases, DevDataOps, microservices, and related topics.