Although software engineering can trace its beginnings to a NATO conf- ence in 1968, it cannot be said to have become an empirical science until the 1970s with the advent of the work of Prof. Victor Robert Basili of the University of Maryland. In addition to the need to engineer software was the need to understand software. Much like other sciences, such as physics, chemistry, and biology, software engineering needed a discipline of obs- vation, theory formation, experimentation, and feedback. By applying the scientific method to the software engineering domain, Basili developed concepts like the Goal-Question-Metric method, the Quality-Improvement- Paradigm, and the Experience Factory to help bring a sense of order to the ad hoc developments so prevalent in the software engineering field. On the occasion of Basili’s 65th birthday, we present this book c- taining reprints of 20 papers that defined much of his work. We divided the 20 papers into 6 sections, each describing a different facet of his work, and asked several individuals to write an introduction to each section. Instead of describing the scope of this book in this preface, we decided to let one of his papers, the keynote paper he gave at the International C- ference on Software Engineering in 1996 in Berlin, Germany to lead off this book. He, better than we, can best describe his views on what is - perimental software engineering.

This book gathers chapters from some of the top international empirical software engineering researchers focusing on the practical knowledge necessary for conducting, reporting and using empirical methods in software engineering. Topics and features include guidance on how to design, conduct and report empirical studies. The volume also provides information across a range of techniques, methods and qualitative and quantitative issues to help build a toolkit applicable to the diverse software development contexts

A groundbreaking book in this field, Software Engineering Foundations: A Software Science Perspective integrates the latest research, methodologies, and their applications into a unified theoretical framework. Based on the author's 30 years of experience, it examines a wide range of underlying theories from philosophy, cognitive informatics, denota

Developing secure software requires the integration of numerous methods and tools into the development process, and software design is based on shared expert knowledge, claims, and opinions. Empirical methods, including data analytics, allow extracting knowledge and insights from the data that organizations collect from their processes and tools, and from the opinions of the experts who practice these processes and methods. This book introduces the reader to the fundamentals of empirical research methods, and demonstrates how these methods can be used to hone a secure software development lifecycle based on empirical data and published best practices.

This book discusses important topics for engineering and managing software startups, such as how technical and business aspects are related, which complications may arise and how they can be dealt with. It also addresses the use of scientific, engineering, and managerial approaches to successfully develop software products in startup companies. The book covers a wide range of software startup phenomena, and includes the knowledge, skills, and capabilities required for startup product development; team capacity and team roles; technical debt; minimal viable products; startup metrics; common pitfalls and patterns observed; as well as lessons learned from startups in Finland, Norway, Brazil, Russia and USA. All results are based on empirical findings, and the claims are backed by evidence and concrete observations, measurements and experiments from qualitative and quantitative research, as is common in empirical software engineering. The book helps entrepreneurs and practitioners to become aware of various phenomena, challenges, and practices that occur in real-world startups, and provides insights based on sound research methodologies presented in a simple and easy-to-read manner. It also allows students in business and engineering programs to learn about the important engineering concepts and technical building blocks of a software startup. It is also suitable for researchers at different levels in areas such as software and systems engineering, or information systems who are studying advanced topics related to software business.

This book addresses the challenges in the software engineering of variability-intensive systems. Variability-intensive systems can support different usage scenarios by accommodating different and unforeseen features and qualities. The book features academic and industrial contributions that discuss the challenges in developing, maintaining and evolving systems, cloud and mobile services for variability-intensive software systems and the scalability requirements they imply. The book explores software engineering approaches that can efficiently deal with variability-intensive systems as well as applications and use cases benefiting from variability-intensive systems.

This book presents contemporary empirical methods in software engineering related to the plurality of research methodologies, human factors, data collection and processing, aggregation and synthesis of evidence, and impact of software engineering research. The individual chapters discuss methods that impact the current evolution of empirical software engineering and form the backbone of future research. Following an introductory chapter that outlines the background of and developments in empirical software engineering over the last 50 years and provides an overview of the subsequent contributions, the remainder of the book is divided into four parts: Study Strategies (including e.g. guidelines for surveys or design science); Data Collection, Production, and Analysis (highlighting approaches from e.g. data science, biometric measurement, and simulation-based studies); Knowledge Acquisition and Aggregation (highlighting literature research, threats to validity, and evidence aggregation); and Knowledge Transfer (discussing open science and knowledge transfer with industry). Empirical methods like experimentation have become a powerful means of advancing the field of software engineering by providing scientific evidence on software development, operation, and maintenance, but also by supporting practitioners in their decision-making and learning processes. Thus the book is equally suitable for academics aiming to expand the field and for industrial researchers and practitioners looking for novel ways to check the validity of their assumptions and experiences. Chapter 17 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.