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.

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."

The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions.

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community's leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.

Get the most out of this foundational reference and improve the productivity of your software teams. This open access book collects the wisdom of the 2017 "Dagstuhl" seminar on productivity in software engineering, a meeting of community leaders, who came together with the goal of rethinking traditional definitions and measures of productivity. The results of their work, Rethinking Productivity in Software Engineering, includes chapters covering definitions and core concepts related to productivity, guidelines for measuring productivity in specific contexts, best practices and pitfalls, and theories and open questions on productivity. You'll benefit from the many short chapters, each offering a focused discussion on one aspect of productivity in software engineering. Readers in many fields and industries will benefit from their collected work. Developers wanting to improve their personal productivity, will learn effective strategies for overcoming common issues that interfere with progress. Organizations thinking about building internal programs for measuring productivity of programmers and teams will learn best practices from industry and researchers in measuring productivity. And researchers can leverage the conceptual frameworks and rich body of literature in the book to effectively pursue new research directions. What You'll Learn Review the definitions and dimensions of software productivity See how time management is having the opposite of the intended effect Develop valuable dashboards Understand the impact of sensors on productivity Avoid software development waste Work with human-centered methods to measure productivity Look at the intersection of neuroscience and productivity Manage interruptions and context-switching Who Book Is For Industry developers and those responsible for seminar-style courses that include a segment on software developer productivity. Chapters are written for a generalist audience, without excessive use of technical terminology.

Software development results in a huge amount of data: changes to source code are recorded in version archives, bugs are reported to issue tracking systems, and communications are archived in e-mails and newsgroups. This book presents techniques for mining version archives and bug databases to understand and support software development.

This volume comprises twelve essays on the ritual landscape of Anatolia, together aiming to explore the phenomenon of sacred landscape comparatively over a wide timespan. Areas featured include Iron Age and Roman Phrygia, Bronze Age Miletus, 5th century BC Ionia, Hellenistic Pergamon, the Latmos mountains from prehistory to Byzantine, the Sanctuary of Leto at Xanthos, colonization of the Sinop promontory, Middle Byzantine Cappadocia and Armenia, and modern Yezidi conceptions of sacred space.

Erfolgreiche Engagements in China gr nden nicht ausschlie lich auf wirtschaftlichen, sondern in hohem Ma e auf interkulturellen Kenntnissen. Aufgrund der gro en Unterschiede speziell zwischen der chinesischen und deutschen Kultur, h ngt der Erfolg einer Unternehmung in China in entscheidender Weise von der kulturellen Kompetenz des eingesetzten Managements ab. Gro e deutsche Unternehmen sind im Gegensatz zu kleineren mittelst ndischen Betrieben schon l ngere Zeit sehr erfolgreich in China. Klein- und Mittelbetriebe scheuen meist "instinktiv" ein Engagement in China, da die gro e kulturelle Distanz un berwindbar scheint. Zus tzlich erscheint die Art, wie in China Gesch fte abgewickelt werden als undurchschaubar und damit mit gro em Risiko verbunden. Weitere Punkte sind die mangelnde Information ber den chinesischen Markt, die unklare Rechtssituation, schwierige Personalsuche und die offensichtliche Sprachbarriere. Bei genauer Betrachtung l sst sich feststellen, dass bei ausreichender Vorbereitung der kulturellen Besonderheiten das angesprochene Risiko deutlich sinkt und ein gro er Schritt hin zu einem erfolgreichen Engagement in China getan ist. Die Auseinandersetzung mit den in der vorliegenden Arbeit Problemfeldern ist allerdings erforderlich, handelt es sich hierbei zumeist um typische Gegebenheiten Chinas. Gerade auf dem Gebiet der Produktpiraterie besteht noch gro er Aufholbedarf in der chinesischen Rechtkultur und sollte in jeden Fall besondere Ber cksichtigung bei geplanten Unternehmungen in China sein. Im Ergebnis ist die zum Teil verbreitete Scheu vor einer Unternehmung in China nicht gerechtfertigt; bei ausreichender Vorbereitung, Bewusstwerdung der m glichen Risiken und der Auseinandersetzung mit der chinesischen Kultur steht einem erfolgreichen Engagement in China nichts im Wege. Ziel dieser Arbeit war es, dem Leser hilfreiche Informationen f r ein geplantes Engagement in China zu bieten. Anhand von Beispielen wurde versucht, auf die speziellen Anforderu