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Books in Software engineering

  • Agile Systems Engineering with SysML v2 and AI

    • 2nd Edition
    • October 1, 2026
    • Bruce Powel Douglass
    • English
    Agile Systems Engineering, Second Edition presents a vision of systems engineering where precise specification of requirements, structure, and behavior meet larger concerns such as safety, security, reliability, and performance in an agile engineering context. World-renowned author and speaker Dr. Bruce Powel Douglass incorporates agile methods and model-based systems engineering (MBSE) to define the properties of entire systems while avoiding errors that can occur when using traditional textual specifications. Dr. Douglass covers the lifecycle of systems development, including requirements, analysis, design, and the handoff to specific engineering disciplines.In addition to agile SE, the workflows and practices of Model-Driven Dev Sec Ops (MDDSO) is introduced and discussed. In this updated edition, all examples and discussion use SysML v2 and are rendered in the Cameo System Modeler tool. Throughout the book, Dr. Douglass couples agile methods with SysML and MBSE to arm system engineers with the conceptual and methodological tools they need to avoid specification defects and improve system quality while simultaneously reducing the effort and cost of systems engineering.

  • Digital Twins

    Core Principles and AI Integration
    • 1st Edition
    • June 1, 2026
    • Bedir Tekinerdogan + 1 more
    • English
    Digital Twins: Core Principles, System Engineering, and AI Integration provides a comprehensive overview of digital twin technology, a cutting-edge innovation that bridges the physical and digital worlds. The book addresses common challenges such as data integration, security, scalability, and the alignment of digital twin models with actual physical processes. After presenting core concepts of digital twins for software engineering, the book discusses integration with advanced digital solutions such as AI, IoT, Cloud computing, Big Data Analytics, and Extended Reality (XR). Next, the authors provide readers with a thorough presentation of digital twins' applications in a variety of settings and industry/research topics.Finally, the book concludes with a discussion of challenges and solutions, along with future trends in digital twins research and development. As digital twin technology evolves, its integration with various advanced digital solutions is becoming essential for achieving real-time insights and autonomous decision-making. Challenges include understanding the interoperability of these technologies, managing data complexity, ensuring security, and optimizing for low-latency environments.

  • Pioneering Autonomous Technology: A Deep Dive into Hyper Automation

    • 1st Edition
    • Volume 143
    • May 1, 2026
    • English
    Pioneering Autonomous Technology: A Deep Dive into Hyper Automation, Volume 143 in the Advances in Computers series, showcases the transformative role of artificial intelligence, deep learning, and machine learning in creating safer, more efficient, and socially inclusive environments. This volume explores how autonomous technologies are reshaping healthcare, transportation, agriculture, and urban living. Chapters cover important topics such as a Survey on Deep Learning Based Autonomous Medical Diagnosis Models for Chronic Disease Identification, Human-Robot Interaction: Ensuring Safe and Effective Coordination, Blazing Trails: Cutting-Edge Technologies Revolutionizing Forest Fire Screening, Connected Communities: Fostering Social Inclusion and Equity in Smart Cities through IoT Integration, and much more.Additional chapters cover a Preliminary Study of Retinal Biomarkers Detection on Fundus Images for the Diagnosis of Alzheimer’s and Parkinson’s Diseases, Deep Learning Driven Classification of Sweet Lime Leaves: A Superior CNN Approach for Agricultural Automation, Machine Learning in Healthcare: Advancements, Applications, and Challenges, Feature Extraction and Object Recognition in Autonomous Systems, The Future of Autonomous Vehicles: Industry Trends, Technologies, and Challenges, Autonomous Systems: Shaping the Future of Industries and Transportation, and many other topics that will be of interest to readers.

  • AI, Blockchain and Social Network on Urban Crisis Management: Leveraging Emerging Technologies for Disaster Response and Resilience in Smart Cities

    • 1st Edition
    • Volume 142
    • April 1, 2026
    • English
    AI, Blockchain and Social Network on Urban Crisis Management: Leveraging Emerging Technologies for Disaster Response and Resilience in Smart Cities, Volume 142 in the Advances in Computers series, examines how cutting-edge digital technologies are reshaping disaster preparedness and response. This volume highlights the convergence of artificial intelligence, blockchain, and social network analysis to predict, prevent, and manage urban crises. Chapters explore Nutrient Recovery from Livestock Effluent in a Circular Economy Approach, Leveraging Artificial Intelligence for Deforestation Monitoring and Conservation in the Colombian Amazon, Experimental and Theoretical Investigation of Diffusion in Hydrocarbon-Based Dense Fluids for Enhanced Oil Recovery, and much more.Additional chapters cover Navigating the Complexities: Challenges, Weaknesses, and Hurdles in Integrating AI, Blockchain, and Social Networks for Urban Crisis Management, Educating Europe's Guardians: Ethical AI and Emerging Technologies in Law Enforcement Training for Urban Crisis Management, The Intersection of AI, Blockchain, and Social Networks in Urban Crises Management, Application of Graph Theory in Disaster Management for Efficient Handling and Mobilizing of Resources and Logistics, Analyzing the Role of Social Networks in Urban Crisis Management through Citizen Participation and Smart Technological Deployment, and many more important and timely topics.

  • Mastering DevOps

    A Cloud Engineering and Data Science Perspective
    • 1st Edition
    • April 1, 2026
    • Chinmaya Kumar Dehury + 1 more
    • English
    Mastering DevOps: A Cloud Engineering and Data Science Perspective addresses the challenge of understanding and implementing DevOps in an era of rapid technological advancement where cloud-based infrastructure and data science applications have become integral to many organizations. The book covers the specific requirements of these fields, such as scalability, automation, and managing large-scale data and containerized applications. Content focuses on DevOps principles while integrating core technologies such as cloud computing, microservices, and continuous integration/continuo... delivery (CI/CD). Additionally, the book provides coverage of a DevOps approach tailored to data science by covering recent advancements and explaining their relevance in a DevOps environment. Specific topics cover fundamental principles, including history, planning, and essential tools like Git, introduce the core technologies and architectures that power modern DevOps, such as microservices, cloud computing, and containerization, and focus on the practical implementation of DevOps, exploring key practices like continuous integration, automation, and monitoring. Finally, the book delves into advanced topics and future trends, such as deployment strategies and the extension of DevOps principles to data science and other narrowed-down domains.

  • Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices

    • 1st Edition
    • Volume 141
    • February 19, 2026
    • English
    Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices, Volume 141 in the Advances in Computers series, explores the convergence of artificial intelligence, machine learning, and modern software engineering practices. Chapters in this new release include Demystifying the Cloud-native Artificial Intelligence (CNAI) Paradigm, Articulating Machine and Deep Learning Models for Next-Generation Software Development, Delineating Artificial Intelligence (AI) and Its Potentials for Automated Software Engineering, Leveraging Machine and Deep Learning (ML/DL) Algorithms towards AI Models for Automating Software Development, and more.Other sections cover Artificial Intelligence (AI) Technologies and Tools for Accelerated Software Development, Demystifying the Agentic AI Paradigm for Accelerated Software Engineering, Detailing AI Techniques and Tools for Software Engineering Acceleration and Automation, Generative AI Tools for Accelerated Software Engineering, Empowering Software Engineering Automation through Explainable AI, and much more.

  • Engineering Generative AI-Based Software

    • 1st Edition
    • January 5, 2026
    • Miroslaw Staroń
    • English
    Engineering Generative-AI Based Software discusses both the process of developing this kind of AI-based software and its architectures, combining theory with practice. Sections review the most relevant models and technologies, detail software engineering practices for such systems, e.g., eliciting functional and non-functional requirements specific to generative AI, explore various architectural styles and tactics for such systems, including different programming platforms, and show how to create robust licensing models. Finally, readers learn how to manage data, both during training and when generating new data, and how to use generated data and user feedback to constantly evolve generative AI-based software.As generative AI software is gaining popularity thanks to such models as GPT-4 or Llama, this is a welcomed resource on the topics explored. With these systems becoming increasingly important, Software Engineering Professionals will need to know how to overcome challenges in incorporating GAI into the products and programs they develop.

  • Consensus

    Fueling Blockchain Innovation and DApp Expansion
    • 1st Edition
    • November 30, 2025
    • Ali Ahmadian + 3 more
    • English
    Consensus: Fueling Blockchain Innovation and DApp Expansion explores the complexities of consensus mechanisms in order to shed light on emerging trends, best practices, and real-world applications that can fuel blockchain innovation while encouraging the dissemination of DApps across various industries. Additionally, the book bridges a crucial gap in the literature by providing in-depth insights into the role of consensus mechanisms in shaping the future of blockchain technology and decentralized applications. This book delves into the fundamentals of blockchain technology along with the roles and significance of vital consensus mechanisms, their underlying principles, formal specifications, functional characteristics, architecture, frameworks, and potential across diverse blockchain applications. Moreover, the book meticulously explores classification, performance metrics, and design parameters. It offers a comprehensive comparative analysis of these mechanisms, shedding light on their computational and communication complexity, strengths, weaknesses, and suitability. Additionally, the book delves into future research directions, highlighting emerging trends and areas requiring further investigation. It also addresses the efforts underway to address existing challenges and open issues within the realm of consensus mechanisms, ensuring a comprehensive understanding of the state-of-the-art in this pivotal aspect of blockchain technology. Due to the wide range of availability and evolving new consensus mechanisms, selecting an optimal and suitable consensus for a specific blockchain application is one of the crucial challenges in the development and innovation of blockchain systems. This book has also a discussion on appropriate selection algorithms based on multi-attribute decision-making for specific blockchain systems and DApps development.

  • The Convergence of Artificial Intelligence (AI) and 6G Communication Networks: The Needs and Implications

    • 1st Edition
    • Volume 139
    • May 22, 2025
    • English
    Advances in Computers, Volume 139 focuses on the convergence of Artificial Intelligence (AI) and 6G communication networks, addressing key advancements and implications across various fields. It explores cybersecurity challenges in 5G networks, solutions for 5G performance evaluation, and the transition to 5G-Advanced. The role of AI in enhancing 6G network performance, resource allocation, and management is discussed alongside the technical foundations of 6G and its ability to power edge AI applications. The volume highlights how 6G will transform industries like logistics through automation and AI-driven decision-making, while also covering strategic management perspectives on AI-driven innovations. Sustainability is a key theme, with discussions on energy-efficient cloud and quantum data centers, as well as the integration of green innovations into AI-6G synergy. The metaverse and its reliance on 5G and 6G for immersive experiences are reviewed, alongside the revolutionary potential of quantum computing in 6G networks. The practical applications of AI, such as a CNN-based model for brain tumor detection using 5G edge cloud, and federated learning for 6G, demonstrate the technology's impact on healthcare and data privacy. Additionally, the volume delves into 6G’s role in enabling next-generation metaverse systems and AI-powered telemedicine, while providing insights into the architecture, communication systems, and industrial use cases of 6G. It concludes by summarizing the advancements, advantages, and challenges of 6G, offering a comprehensive view of its future impact on global connectivity.

  • Quantum Process Algebra

    • 1st Edition
    • March 6, 2025
    • Yong Wang
    • English
    Quantum Process Algebra introduces readers to the algebraic properties and laws for quantum computing. The book provides readers with all aspects of algebraic theory for quantum computing, including the basis of semantics and axiomatization for quantum computing. With the assumption of a quantum system, readers will learn to solve the modeling of the three main components in a quantum system: the unitary operator, quantum measurement, and quantum entanglement, with full support of quantum and classical computing in closed systems. Next, the book establishes the relationship between probabilistic quantum bisimilarity and classical probabilistic bisimilarity, including strong probabilistic bisimilarity and weak probabilistic bisimilarity, which makes an axiomatization of quantum processes possible. With this framework, quantum and classical computing mixed processes are unified with the same structured operational semantics. Finally, the book establishes a series of axiomatizations of quantum process algebras. These process algebras support nearly all the main computation properties. Quantum and classical computing in closed quantum systems are unified with the same equational logic and the same structured operational semantics under the framework of ACP-like probabilistic process algebra. This unification means that the mathematics in the book can be used widely for verification of quantum and classical computing mixed systems, for example, most quantum communication protocols. ACP-like axiomatization also inherits the advantages of ACP, for example, and modularity means that it can be extended in an elegant way.