Perspectives on Data Science for Software Engineering - Tapa blanda

Acerca de los autores

Tim Menzies, Full Professor, CS, NC State and a former software research chair at NASA. He has published 200+ publications, many in the area of software analytics. He is an editorial board member (1) IEEE Trans on SE; (2) Automated Software Engineering journal; (3) Empirical Software Engineering Journal. His research includes artificial intelligence, data mining and search-based software engineering. He is best known for his work on the PROMISE open source repository of data for reusable software engineering experiments.

Laurie Williams, Full Professor and Associate Department Head CS, NC State. 180+ publications, many applying software analytics. She is on the editorial boards of IEEE Trans on SE; (2) Information and Software Technology; and (3) IEEE Software.

is a researcher in the Research in Software Engineering (RiSE) group at Microsoft Research, adjunct assistant professor at the University of Calgary, and affiliate faculty at University of Washington. He is best known for his work on systematic mining of version archives and bug databases to conduct empirical studies and to build tools to support developers and managers. He received two ACM SIGSOFT Distinguished Paper Awards for his work published at the ICSE '07 and FSE '08 conferences.

De la contraportada

Perspectives on Software Analytics presents best practices of seasoned data miners in software engineering. The spark of this book ignited during 2014s conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At 2014s conference, a repeated question was how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field. 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 insight into the wisdom of the communitys leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics include 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 tips and tricks of the trade; more experienced data scientists will benefit from "war stories" showing what traps to avoid.