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.

This book focuses on software architecture and the value of architecture in the development of long-lived, mission-critical, trustworthy software-systems. The author introduces and demonstrates the powerful strategy of “Managed Evolution,” along with the engineering best practice known as “Principle-based Architecting.” The book examines in detail architecture principles for e.g., Business Value, Changeability, Resilience, and Dependability. The author argues that the software development community has a strong responsibility to produce and operate useful, dependable, and trustworthy software. Software should at the same time provide business value and guarantee many quality-of-service properties, including security, safety, performance, and integrity. As Dr. Furrer states, “Producing dependable software is a balancing act between investing in the implementation of business functionality and investing in the quality-of-service properties of the software-systems.” The book presents extensive coverage of such concepts as: Principle-Based Architecting Managed Evolution Strategy The Future Principles for Business Value Legacy Software Modernization/Migration Architecture Principles for Changeability Architecture Principles for Resilience Architecture Principles for Dependability The text is supplemented with numerous figures, tables, examples and illustrative quotations. Future-Proof Software-Systems provides a set of good engineering practices, devised for integration into most software development processes dedicated to the creation of software-systems that incorporate Managed Evolution.

Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity. The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors. Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques Presents case studies involving enterprise, business, and government service deployment of big data applications Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data

Architectural design is a crucial first step in developing complex software intensive systems. Early design decisions establish the structures necessary for achieving broad systemic properties. However, today's organizations lack synergy between software their development processes and technological methodologies. Providing a thorough treatment of

System developers, stakeholders, decision makers, policymakers and academics will find this book a one-stop resource highlighting the core issues for all those involved in dependability in a complex computer-based environment.

Modern-day projects require software and systems engineers to work together in realizing architectures of large and complex software-intensive systems. To date, the two have used their own tools and methods to deal with similar issues when it comes to the requirements, design, testing, maintenance, and evolution of these architectures. Software and Systems Architecture in Action explores practices that can be helpful in the development of architectures of large-scale systems in which software is a major component. Examining the synergies that exist between the disciplines of software and systems engineering, it presents concepts, techniques, and methods for creating and documenting architectures. The book describes an approach to architecture design that is driven from systemic quality attributes determined from both the business and technical goals of the system, rather than just its functional requirements. This architecture-centric design approach utilizes analytically derived patterns and tactics for quality attributes that inform the architect's design choices and help shape the architecture of a given system. The book includes coverage of techniques used to assess the impact of architecture-centric design on the structural complexity of a system. After reading the book, you will understand how to create architectures of systems and assess their ability to meet the business goals of your organization. Ideal for anyone involved with large and complex software-intensive systems, the book details powerful methods for engaging the software and systems engineers on your team. The book is also suitable for use in undergraduate and graduate-level courses on software and systems architecture as it exposes students to the concepts and techniques used to create and manage architectures of software-intensive systems.

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.