Contemporary Empirical Methods in Software Engineering [electronic resource] /

1. Introduction: The Evolution of Empirical Methods in Software Engineering -- Part I: Study Strategies -- 2. Guidelines for Conducting Software Engineering Research -- 3. Preliminary Guidelines for Case Survey Research in Software Engineering -- 4. Challenges in Survey Research -- 5. The Design Science Paradigm as a Frame for Empirical Software Engineering -- Part II: Data Collection, Production, and Analysis -- 6. Biometric Measurement in Software Engineering -- 7. Empirical Software Engineering Experimentation with Human Computation -- 8. Data Science and Empirical Software Engineering -- 9. Optimization in Software Engineering - A Pragmatic Approach -- 10. The Role of Simulation-based Studies in Software Engineering Research -- 11. Bayesian data analysis in empirical software engineering-The case of missing data -- Part III: Knowledge Acquisition and Aggregation -- 12. Automating Systematic Literature Reviews -- 13. Rapid Reviews in Software Engineering -- 14. Benefitting fromthe Grey Literature in Software Engineering Research -- 15: Systematic Assessment of Threats to Validity in Software Engineering Secondary Studies -- 16. Evidence Aggregation in Software Engineering -- Part IV: Knowledge Transfer -- 17. Open Science in Software Engineering -- 18. Practical industry co-production and technology and knowledge interchange.

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.