Thomas Zimmermann – författare

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.

Presents best practices, hints, and tips to analyze data and apply tools in data science projects Presents research methods and case studies that have emerged over the past few years to furtherunderstanding of software data Shares stories from the trenches of successful data science initiatives in industry

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.

Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains

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 LearnReview the definitions and dimensions of software productivitySee how time management is having the opposite of the intended effectDevelop valuable dashboardsUnderstand the impact of sensors on productivityAvoid software development wasteWork with human-centered methods to measure productivityLook at the intersection of neuroscience and productivityManage interruptions and context-switchingWho Book Is ForIndustry 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.

Proceedings from the 2022 Metal Ages colloquium in Ankara. Topics include water supply and management, copper metallurgy, pottery, and combat techniques, spanning the Chalcolithic to Late Iron Age across Iran to Iberia, with a focus on artefact archaeometry.

So it is hardly a decade ago that presupposition theory has been cast into a promising theoretical form. And as this provided an inspiring starting point for further research, we thought that, at the turn of the century, the time had come to discuss what the new theory had taught us and which promising further perspectives had been opened up. This was the motivation behind the conference on "Presupposition" which we convened in Stuttgart in October 2000, and which gave rise to the papers collected in this volume. The conference was funded by the Deutsche Forschungsgemeinschaft, which we gratefully acknowledge. Undoubtedly, presupposition theory is a major chapter in the success story of dynamic semantics. A conference on the topic thus also seemed to us the ideal birthday present for one of the founding fathers of dynamic semantics, our teacher and friend Hans Kamp, on his 60th birthday. To him we dedicate the volume as an expression of our gratitude for his untiring effort to make us understand.

Recommendation Systems in Software Engineering

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.

Das vorliegende Handbuch bietet erstmals eine umfassende, interdisziplinäre Auseinandersetzung mit den Handlungsfeldern des stadtregionalen Flächenmanagements. WissenschaftlerInnen und PraktikerInnen aus unterschiedlichen Disziplinen wie der Raum- und Stadtplanung, Geographie, Soziologie und den Wirtschaftswissenschaften diskutieren Konzepte und Vorgehensweisen und reflektieren darüber hinaus Praxiserfahrungen des Flächenmanagements in unterschiedlichen Stadtregionen Deutschlands und dem Ausland.Das problembasierte und lösungsorientierte Handbuch wird durch eine eigens von den Herausgebern entwickelte Systematisierung für Instrumente des Flächenmanagements gegliedert. Diese greift die unterschiedlichen Treiber für die Zersiedelung auf: Regulative Instrumente wie Regionalpläne bilden den rechtlich bindenden Rahmen für sämtliche Akteure in Stadtregionen. Anreizbasierte Instrumente wie Flächenzertifikatehandel setzen stärker an den tatsächlichen Ursachen für Zersiedelung an, indem sie erwünschtes Handeln belohnen und unerwünschtes bestrafen. Informatorische Instrumente wie Monitoringsysteme erlauben es wiederum, datenbasierte Entscheidungen zu treffen. Kooperative Instrumente in Form von Austausch- und Partizipationsformaten können indes zum Flächenmanagement beitragen, indem sie in einem prozessorientierten Vorgehen das Handeln der Akteure auf gemeinsame Ziele ausrichten. Organisationale Instrumente haben schlussendlich zum Ziel, stadtregionale Organisationen zu etablieren, um die anderen Instrumententypen langfristig über administrative Grenzen hinweg steuern zu können.Das Buch richtet sich gleichermaßen an StudentInnen, WissenschaftlerInnen und PraktikerInnen aus der Stadt-, Regional- und Landesplanung.