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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.
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.
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