Sharing Data And Models In Software Engineering
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Sharing Data and Models in Software Engineering
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. - Shares the specific experience of leading researchers and techniques developed to handle data problems in the realm of software engineering - Explains how to start a project of data science for software engineering as well as how to identify and avoid likely pitfalls - Provides a wide range of useful qualitative and quantitative principles ranging from very simple to cutting edge research - Addresses current challenges with software engineering data such as lack of local data, access issues due to data privacy, increasing data quality via cleaning of spurious chunks in data
Contemporary Empirical Methods in Software Engineering
This book presents contemporary empirical methods in software engineering related to the plurality of research methodologies, human factors, data collection and processing, aggregation and synthesis of evidence, and impact of software engineering research. The individual chapters discuss methods that impact the current evolution of empirical software engineering and form the backbone of future research. Following an introductory chapter that outlines the background of and developments in empirical software engineering over the last 50 years and provides an overview of the subsequent contributions, the remainder of the book is divided into four parts: Study Strategies (including e.g. guidelines for surveys or design science); Data Collection, Production, and Analysis (highlighting approaches from e.g. data science, biometric measurement, and simulation-based studies); Knowledge Acquisition and Aggregation (highlighting literature research, threats to validity, and evidence aggregation); and Knowledge Transfer (discussing open science and knowledge transfer with industry). Empirical methods like experimentation have become a powerful means of advancing the field of software engineering by providing scientific evidence on software development, operation, and maintenance, but also by supporting practitioners in their decision-making and learning processes. Thus the book is equally suitable for academics aiming to expand the field and for industrial researchers and practitioners looking for novel ways to check the validity of their assumptions and experiences. Chapter 17 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Collaborative Software Engineering
Author: Ivan Mistrík
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-03-10
Collaboration among individuals – from users to developers – is central to modern software engineering. It takes many forms: joint activity to solve common problems, negotiation to resolve conflicts, creation of shared definitions, and both social and technical perspectives impacting all software development activity. The difficulties of collaboration are also well documented. The grand challenge is not only to ensure that developers in a team deliver effectively as individuals, but that the whole team delivers more than just the sum of its parts. The editors of this book have assembled an impressive selection of authors, who have contributed to an authoritative body of work tackling a wide range of issues in the field of collaborative software engineering. The resulting volume is divided into four parts, preceded by a general editorial chapter providing a more detailed review of the domain of collaborative software engineering. Part 1 is on "Characterizing Collaborative Software Engineering", Part 2 examines various "Tools and Techniques", Part 3 addresses organizational issues, and finally Part 4 contains four examples of "Emerging Issues in Collaborative Software Engineering". As a result, this book delivers a comprehensive state-of-the-art overview and empirical results for researchers in academia and industry in areas like software process management, empirical software engineering, and global software development. Practitioners working in this area will also appreciate the detailed descriptions and reports which can often be used as guidelines to improve their daily work.