With the growth of public and private data stores and the
emergence of off-the-shelf data-mining technology, recommendation
systems have emerged that specifically address the unique
challenges of navigating and interpreting software engineering
data.
This book collects, structures and formalizes knowledge on
recommendation systems in software engineering. It adopts a
pragmatic approach with an explicit focus on system design,
implementation, and evaluation. The book is divided into three
parts: "Part I - Techniques" introduces basics for building
recommenders in software engineering, including techniques for
collecting and processing software engineering data, but also for
presenting recommendations to users as part of their workflow."Part
II - Evaluation" summarizes methods and experimental designs for
evaluating recommendations in software engineering."Part III -
Applications" describes needs, issues and solution concepts
involved in entire recommendation systems for specific software
engineering tasks, focusing on the engineering insights required to
make effective recommendations. The book is complemented by the
webpage rsse.org/book, which includes free supplemental materials
for readers of this book and anyone interested in recommendation
systems in software engineering, including lecture slides, data
sets, source code, and an overview of people, groups, papers and
tools with regard to recommendation systems in software
engineering.
The book is particularly well-suited for graduate students and
researchers building new recommendation systems for software
engineering applications or in other high-tech fields. It may also
serve as the basis for graduate courses on recommendation systems,
applied data mining or software engineering. Software engineering
practitioners developing recommendation systems or similar
applications with predictive functionality will also benefit from
the broad spectrum of topics covered."
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