Detalles de la obra

Perspectives on data science for software engineering
Menzies, Tim ; ed.; Williams, Laurie ; ed.; Zimmermann, Thomas ; ed.
Pie de imprenta:
Cambridge: Morgan Kaufman, (c)2016
Descripción física:
378 p. ; 24 x 20 cm
Contenido parcial: Perspectives on Data Science for Software Engineering - Software analytics and its application in practice - Seven principles of inductive software engineering: What we do is different - The need for data analysis patterns (in software engineering) - From software data to software theory: The path less traveled - Why theory matters - Mining apps for anomalies - Embrace dynamic artifacts - Mobile app store analytics - The naturalness of software - Advances in release readiness - How to tame your online services - How to Measure Individual Productivity - Stack Traces Reveal Attack Surfaces - Visual analytics for software engineering data - Gameplay data plays nicer when divided into cohorts - A success story in applying data science in practice - There's never enough time to do all the testing you want - The perils of energy mining: measure a bunch, compare just once - Identifying fault-prone files in large industrial software systems - A tailored suit: the big opportunity in personalizing issue tracking.
Referencias bibliográficas:
Incluye Bibliografía.
Ubicación física:
005.1 / MEN
Tipo de material:
[Material Impreso]