The Art and Science of Analyzing Software Data: Analysis Patterns

The Art and Science of Analyzing Software Data: Analysis Patterns provides valuable information on the analysis methods that often transfer between projects, not only the algorithms derived from data analysis, but also the processes and structure that software engineers follow when using algorithms, giving users the foundation they need to extract models, data, or patterns that could be insightful for future projects. This updated volume shares best practices in the field generated by leading data scientists, and collected from their experience training software engineering students and practitioners to master data science. Throughout, the editors highlight the methods that are most useful, and applicable, to the widest range of projects. Presents best practices to analyze data and apply tools, hints, and tips for managing data science projects Presents tactics on how to avoid mistakes commonly made by beginners trying to apply data analysisPresents research methods and case studies that have only emerged over the past few years to further understandingIncludes analysis patterns that highlight which parts of each chapter are most useful for other applications INDICE: TABLE OF CONTENTS (SHORT):1. Data Science 101: Introductory notes 2. Mining apps store data3. Finding project effort 4. Learning changes to software processes 5. Learning how to control power consumptions 6. Text mining on requirements; reverse engineering 7. Reverse engineering of design patterns8. Reverse engineering of the API between components 9. Defect prediction10. Learning the social network that connects members of a development team 11. Models which can find effective project changes12. Appendix: tiny tool tips to get you started

• ISBN: 978-0-12-411519-4
• Editorial: Morgan Kaufmann
• Encuadernacion: Rústica
• Páginas: 688
• Fecha Publicación: 29/07/2015
• Nº Volúmenes: 1
• Idioma: Inglés