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Recommendation Systems in Software Engineering (Paperback, Softcover reprint of the original 1st ed. 2014)
Loot Price: R5,981
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Recommendation Systems in Software Engineering (Paperback, Softcover reprint of the original 1st ed. 2014)
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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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