Sharing Data and Models in Software Engineering (Paperback)

Tim Menzies, Ekrem Kocaguneli, Burak Turhan, Leandro Minku, Fayola Peters

  • 出版商: Morgan Kaufmann
  • 出版日期: 2014-12-15
  • 售價: $3,100
  • 貴賓價: 9.5$2,945
  • 語言: 英文
  • 頁數: 406
  • 裝訂: Paperback
  • ISBN: 0124172954
  • ISBN-13: 9780124172951
  • 相關分類: 軟體工程
  • 立即出貨 (庫存=1)

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商品描述

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

商品描述(中文翻譯)

《資料科學應用於軟體工程:分享資料與模型》提供了在不同專案間重複使用資料和模型以產生有用且相關的結果的指導和程序。本書首先介紹了實用的經驗教訓和警示,針對初學者資料科學家在軟體工程領域的相關知識。接著,本書確定了當代軟體工程中與資料和模型相關的關鍵問題。您將學習如何將其他組織的資料適應到本地問題中,如何挖掘私有化的資料,如何修剪虛假的資訊,簡化複雜的結果,以及如何為新平台更新模型等等。各章節分享了廣泛適用的實驗結果,並以實踐者專注的領域專業知識進行討論,並強調了最有用且適用於各種專案的方法。每一章節都由知名專家撰寫,提供了對軟體工程中資料科學面臨的問題的最新解決方案。在整本書中,編者們分享了他們在培訓軟體工程學生和從業人員掌握資料科學的經驗中收集到的最佳實踐,並強調了最有用且適用於各種專案的方法。

本書的特點包括:
- 分享了領先研究人員的具體經驗和在軟體工程領域處理資料問題的技術
- 解釋了如何啟動一個軟體工程資料科學專案,以及如何識別和避免可能的問題
- 提供了一系列從非常簡單到尖端研究的有用的定性和定量原則
- 解決了軟體工程資料面臨的當前挑戰,例如缺乏本地資料、由於資料隱私而產生的存取問題,以及通過清理資料中的虛假塊來提高資料品質。