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This open access book provides a basic introduction to feature modelling and analysis as well as to the integration of AI methods with feature modelling. It is intended as an introduction for researchers and practitioners who are new to the field and will also serve as a state-of-the-art reference to this audience. While focusing on the AI perspective, the book covers the topics of feature modelling (including languages and semantics), feature model analysis, and interacting with feature model configurators. These topics are discussed along the AI areas of knowledge representation and reasoning, explainable AI, and machine learning.

This book provides a comprehensive, empirically grounded exploration of how Generative AI is reshaping the landscape of software development. It emphasizes the empirical evaluation of Generative AI tools in real-world scenarios, offering insights into their practical efficacy, limitations, and impact. By presenting case studies, surveys, and interviews from various software development contexts, the book offers a global perspective on the integration of Generative AI, highlighting how these advanced tools are adapted to and influence diverse cultural, organizational, and technological environments. This book is structured to provide a comprehensive understanding of Generative AI and its transformative impact on the field of software engineering. The book is divided into five parts, each focusing on different aspects of Generative AI in software development. As an introduction, Part 1 presents the fundamentals of Generative AI adoption. Part 2 is a collection of empirical studies and delves into the practical aspects of integrating Generative AI tools in software engineering, with a focus on patterns, methodologies, and comparative analyses. Next, Part 3 presents case studies that showcase the application and impact of Generative AI in various software development contexts. Part 4 then examines how Generative AI is reshaping software engineering processes, from collaboration and workflow to management and agile development. Finally, Part 5 looks towards the future, exploring emerging trends, future directions, and the role of education in the context of Generative AI. The book offers diverse perspectives as it compiles research and experiences from various countries and software development environments. It also offers non-technical discussions about Generative AI in management, teamwork, business and education. This way, it is intended for both researchers in software engineering and for professionals in industry who want to learn about the impactof Generative AI on software development.

This book provides an overview of software security analysis in a DevOps cycle including requirements formalisation, verification and continuous monitoring. It presents an overview of the latest techniques and tools that help engineers and developers verify the security requirements of large-scale industrial systems and explains novel methods that enable a faster feedback loop for verifying security-related activities, which rely on techniques such as automated testing, model checking, static analysis, runtime monitoring, and formal methods. The book consists of three parts, each covering a different aspect of security engineering in the DevOps context. The first part,'Security Requirements', explains how to specify and analyse security issues in a formal way. The second part,'Prevention at Development Time', offers a practical and industrial perspective on how to design, develop and verify secure applications. The third part,'Protection at Operations', eventually introduces tools for continuous monitoring of security events and incidents. Overall, it covers several advanced topics related to security verification, such as optimizing security verification activities, automatically creating verifiable specifications from security requirements and vulnerabilities, and using these security specifications to verify security properties against design specifications and generate artifacts such as tests or monitors that can be used later in the DevOps process. The book aims at computer engineers in general and does not require specific knowledge. In particular, it is intended for software architects, developers, testers, security professionals, and tool providers, who want to define, build, test, and verify secure applications, Web services, and industrial systems.

This book offers a practical introduction to the use of artificial intelligence (AI) techniques to improve and optimise the various phases of the software development process, from the initial project planning to the latest deployment. All chapters were written by leading experts in the field and include practical and reproducible examples. Following the introductory chapter, Chapters 2-9 respectively apply AI techniques to the classic phases of the software development process: project management, requirement engineering, analysis and design, coding, cloud deployment, unit and system testing, and maintenance. Subsequently, Chapters 10 and 11 provide foundational tutorials on the AI techniques used in the preceding chapters: metaheuristics and machine learning. Given its scope and focus, the book represents a valuable resource for researchers, practitioners and students with a basic grasp of software engineering.

This book guides the reader through a design process that was tested and optimized in companies and design bureaus. It not only smoothly integrates modern product development techniques, but also addresses, for each phase, issues related to the management of intangible assets. There are several books on the product design process, as well as on the development of innovative products in general. However, none of them addresses how to integrate the engineering techniques with the necessary aspects of Intellectual Property Management. With a focus on software intensive products in general, the book presents a meta-process that adapts to product design in any area where the software element is an important factor in product functionality and innovation.

This book highlights recent research advances in various domains related to software ecosystems such as library reuse, collaborative development, cloud computing, open science, sentiment analysis and machine learning. A key aspect of software ecosystems is that software products belong to ever more interdependent networks of co-evolving software components. The ever-increasing importance of social coding platforms has made software ecosystems indispensable to software practitioners, in commercial as well as open-source settings.The book starts with an introductory chapter that provides a historical account of the origins of software ecosystems. It provides the necessary context about the domain of software ecosystems by highlighting its different perspectives, definitions, and representations. It also exemplifies the variety of software ecosystems that have emerged during the previous decades. The remaining book is composed of five parts: Part I contains two chapters on software ecosystem representations, Part II two chapters that focus on complementary ways and techniques of analyzing software ecosystems. Next, Part III includes two chapters that focus on aspects related to the evolution within software ecosystems, while Part IV looks at workflow automation and infrastructure-as-code ecosystems. Finally, Part V focuses on ecosystems for software modeling and for data-intensive software.This book is intended for researchers and practitioners interested in data mining, tooling, and empirical analysis of software ecosystems. The reader will appreciate chapters that cover a wide spectrum of social and technical aspects of software ecosystems, each including an overview of the state of the art.Chapter 2 The Software Heritage Open Science Ecosystem is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.