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Software Quality Assurance in Large Scale and Complex Software-intensive Systems presents novel and high-quality research related approaches that relate the quality of software architecture to system requirements, system architecture and enterprise-architecture, or software testing. Modern software has become complex and adaptable due to the emergence of globalization and new software technologies, devices and networks. These changes challenge both traditional software quality assurance techniques and software engineers to ensure software quality when building today (and tomorrow's) adaptive, context-sensitive, and highly diverse applications.

This edited volume presents state of the art techniques, methodologies, tools, best practices and guidelines for software quality assurance and offers guidance for future software engineering research and practice. Each contributed chapter considers the practical application of the topic through case studies, experiments, empirical validation, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited, to: quality attributes of system/software architectures; aligning enterprise, system, and software architecture from the point of view of total quality; design decisions and their influence on the quality of system/software architecture; methods and processes for evaluating architecture quality; quality assessment of legacy systems and third party applications; lessons learned and empirical validation of theories and frameworks on architectural quality; empirical validation and testing for assessing architecture quality.

Focused on quality assurance at all levels of software design and development Covers domain-specific software quality assurance issues e.g. for cloud, mobile, security, context-sensitive, mash-up and autonomic systems Explains likely trade-offs from design decisions in the context of complex software system engineering and quality assurance Includes practical case studies of software quality assurance for complex, adaptive and context-critical systems

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics.

This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.

Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions

Multi-Paradigm Modelling for Cyber-Physical Systems explores modeling and analysis as crucial activities in the development of Cyber-Physical Systems, which are inherently cross-disciplinary in nature and require distinct modeling techniques related to different disciplines, as well as a common background knowledge. This book will serve as a reference for anyone starting in the field of CPS who needs a solid foundation of modeling, including a comprehensive introduction to existing techniques and a clear explanation of their advantages and limitations. This book is aimed at both researchers and practitioners who are interested in various modeling paradigms across computer science and engineering. Identifies key problems and offers solution approaches as well as tools which have been developed or are necessary for modeling paradigms across cyber physical systems Explores basic theory and current research topics, related challenges, and research directions for multi-paradigm modeling Provides a complete, conceptual overview and framework of the research done by the MPM4CPS working groups and the different types of modeling paradigms developed

Management and Engineering of Critical Infrastructures focuses on two important aspects of CIS, management and engineering. The book provides an ontological foundation for the models and methods needed to design a set of systems, networks and assets that are essential for a society's functioning, and for ensuring the security, safety and economy of a nation. Various examples in agriculture, the water supply, public health, transportation, security services, electricity generation, telecommunication, and financial services can be used to substantiate dangers. Disruptions of CIS can have serious cascading consequences that would stop society from functioning properly and result in loss of life.Malicious software (a.k.a., malware), for example, can disrupt the distribution of electricity across a region, which in turn can lead to the forced shutdown of communication, health and financial sectors. Subsequently, proper engineering and management are important to anticipate possible risks and threats and provide resilient CIS. Although the problem of CIS has been broadly acknowledged and discussed, to date, no unifying theory nor systematic design methods, techniques and tools exist for such CIS. Presents key problems, solution approaches and tools developed, or necessary for, assessing and managing critical infrastructures Investigates basic theory and foundations of current research topics, related challenges, and future research directions for CIS Offers an interdisciplinary approach, considering management, engineering and specific challenges (dynamism and scalability) surrounding the digitization of CIS services

Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge. Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems.Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.

System of Systems Engineering: Innovations, Challenges, and Future Directions focuses on the many aspects of System of Systems Engineering. Part I, Foundations of System of Systems Engineering, introduces the field, characterizes and classifies SoS, and discusses key concepts. Part II, Governance and Management of SoSE, covers strategic governance, policy and regulatory frameworks, and leadership and decision-making in SoSE projects. Part III, Methodologies and Tools, explores systems thinking and modeling approaches, lifecycle management, and interoperability and integration strategies. Part IV, AI and System of Systems Engineering, delves into leveraging AI for enhanced decision-making, machine learning applications, AI-driven automation and control, and ethical considerations.Finally, Part V, Case Studies and Emerging Challenges, presents real-world applications in defense and aerospace, smart cities, healthcare, environmental and energy systems, and discusses future directions and research opportunities. This book offers significant benefits to graduate students, researchers, and professionals in software engineering, systems engineering, aerospace engineering, defense, telecommunications, and other fields where SoSE is relevant.Addresses governance, management, and methodological challenges in SoSEExplores AI applications and ethical considerations in complex systemsProvides real-world case studies across multiple industriesPresents content that is suitable for advanced courses and professional development in SoSE

Digital Twins: Core Principles and AI Integration offers a structured and up-to-date overview of digital twin technology, combining foundational principles with the rapidly growing role of artificial intelligence (AI). This book introduces the core concepts, modeling approaches, and software and systems engineering foundations needed to design and implement digital twins effectively. It then explores architectural methods, lifecycle management, interoperability, and the alignment between physical systems and their digital representations. A central part of this book focuses on data science and AI-enabled digital twins, demonstrating how machine learning, deep learning, generative AI, and autonomous agents enhance predictive analytics, optimization, anomaly detection, and automated decision-making. Integration with Internet of Things (IoT), cloud-edge infrastructures, big data analytics, and XR technologies further shows how intelligent digital twins evolve into adaptive and interactive systems. Real-world applications from manufacturing, agriculture, food systems, energy, mobility, healthcare, and urban environments illustrate the practical value of AI-driven digital twins. This book concludes with key challenges and future directions, including trustworthy AI, security, data governance, and the scaling of digital twin ecosystems.Clear progression from foundations and architecture to AI integration and real-world applications Dedicated focus on how AI transforms digital twin intelligence and autonomy Case studies demonstrating implementation across major sectorsInsight into future trends, research challenges, and opportunities

Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge. Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems.Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.

This book constitutes the proceedings of the 6th International Conference on Internet of Things, ICIOT 2021, held virtually as part of SCF 2021, during December 10-14, 2021.The 8 full papers presented in this volume were carefully reviewed and selected from numerous submissions.The conference Internet of Things (ICIOT 2021) covers state-of-the-art technologies and best practices of Internet of Things, as well as emerging standards and research topics which would define the future of Internet of Things.

This book constitutes the proceedings of the 7th International Conference on Internet of Things, ICIOT 2022, held in Honolulu, USA, as part of SCF 2022, during December 10-14, 2022.The 9 full papers presented in this volume were carefully reviewed and selected from 17 submissions.The conference Internet of Things (ICIOT 2022) covers state-of-the-art technologies and best practices of Internet of Things, as well as emerging standards and research topics which would define the future of Internet of Things.

This book constitutes the refereed proceedings of the 17th International Conference on Software Architecture, ECSA 2023, held in Istanbul, Turkey, in September 2023.The 16 full papers and the 9 short papers included in this volume were carefully reviewed and selected from 71 submissions. They address the most recent, innovative, and significant findings and experiences in the field of software architecture research and practice.

This book constitutes the refereed proceedings of the tracks and workshops which complemented the 17th European Conference on Software Architecture, ECSA 2023, held in Istanbul, Turkey, in September 2023. The 29 full papers included in this book were carefully reviewed and selected from 32 submissions. DE Industry Track;

This book constitutes the refereed proceedings of the 10th International Conference on Internet of Things – ICIOT 2025, held as part of the Services Conference Federation, SCF 2025, in Hong Kong, China during September 27–30, 2025.The 8 full papers included in this book were carefully reviewed and selected from 11 submissions. These papers focus on current research in various aspects of IoT technologies and applications, including sensors and other types of sensing devices, wired and wireless networks, platforms and tools, data processing/visualization/analysis and integration engines, and other components of traditional IoT that are interconnected through innovative services to realize the value of connected things, people, and virtual Internet spaces.

This book constitutes the proceedings of the 10th European Conference on Software Architecture, ECSA 2016, held in Copenhagen, Denmark, in November/December 2016. The 13 full papers presented together with 12 short papers were carefully reviewed and selected from 84 submissions.