Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects.

This book constitutes the refereed proceedings of the 18th International Conference on Product-Focused Software Process Improvement, PROFES 2017, held in Innsbruck, Austria, in November/December 2017. The 17 revised full papers presented together with 10 short papers, 21 workshop papers. 3 posters and tool demonstrations papers, and 4 tutorials were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on : Agile software Development; Data science and analytics; Software engineering processes and frameworks; Industry relevant qualitative research; User and value centric approaches; Software startups; Serum; Software testing.