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LEADER 00000cam a2200637Ii 4500
001 903401639
003 OCoLC
005 20240129213017.0
006 m o d
007 cr unu||||||||
008 150213s2015 maua ob 000 0 eng d
019 930870739
020 9780124173071
020 0124173071
020 0124172954
020 9780124172951
029 1 DEBBG|bBV042487434
029 1 DEBSZ|b434828327
029 1 GBVCP|b88284119X
035 (OCoLC)903401639|z(OCoLC)930870739
037 CL0500000545|bSafari Books Online
040 UMI|beng|erda|epn|cUMI|dDEBBG|dOCLCF|dDEBSZ|dOCLCQ|dSUE
|dN9V|dCEF|dOCLCQ|dOCLCO|dOCLCQ|dOCLCO|dOCLCL
049 INap
082 04 005.1
082 04 005.1|223
099 eBook O'Reilly for Public Libraries
100 1 Menzies, Tim,|eauthor.
245 10 Sharing data and models in software engineering /|cTim
Menzies, Ekrem Kocaguneli, Leandro Minku, Fayola Peters,
Burak Turhan.|h[O'Reilly electronic resource]
250 First edition.
264 1 Waltham, MA :|bMorgan Kaufmann,|c[2015]
264 4 |c©2015
300 1 online resource (1 volume) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
504 Includes bibliographical references.
505 0 Front Cover; Sharing Data and Models in Software
Engineering; Copyright; Why this book?; Foreword;
Contents; List of Figures; Chapter 1: Introduction; 1.1
Why Read This Book?; 1.2 What Do We Mean by S̀̀haring''?;
1.2.1 Sharing Insights; 1.2.2 Sharing Models; 1.2.3
Sharing Data; 1.2.4 Sharing Analysis Methods; 1.2.5 Types
of Sharing; 1.2.6 Challenges with Sharing; 1.2.7 How to
Share; 1.3 What? (Our Executive Summary); 1.3.1 An
Overview; 1.3.2 More Details; 1.4 How to Read This Book;
1.4.1 Data Analysis Patterns; 1.5 But What About ...?
(What Is Not in This Book); 1.5.1 What About B̀̀ig Data''?
505 8 1.5.2 What About Related Work?1.5.3 Why All the Defect
Prediction and Effort Estimation?; 1.6 Who? (About the
Authors); 1.7 Who Else? (Acknowledgments); Part I: Data
Mining for Managers; Chapter 2: Rules for Managers; 2.1
The Inductive Engineering Manifesto; 2.2 More Rules;
Chapter 3: Rule #1: Talk to the Users; 3.1 Users Biases;
3.2 Data Mining Biases; 3.3 Can We Avoid Bias?; 3.4
Managing Biases; 3.5 Summary; Chapter 4: Rule #2: Know the
Domain; 4.1 Cautionary Tale #1: D̀̀iscovering'' Random
Noise; 4.2 Cautionary Tale #2: Jumping at Shadows; 4.3
Cautionary Tale #3: It Pays to Ask.
505 8 4.4 SummaryChapter 5: Rule #3: Suspect Your Data; 5.1
Controlling Data Collection; 5.2 Problems with Controlled
Data Collection; 5.3 Rinse (and Prune) Before Use; 5.3.1
Row Pruning; 5.3.2 Column Pruning; 5.4 On the Value of
Pruning; 5.5 Summary; Chapter 6: Rule #4: Data Science Is
Cyclic; 6.1 The Knowledge Discovery Cycle; 6.2 Evolving
Cyclic Development; 6.2.1 Scouting; 6.2.2 Surveying; 6.2.3
Building; 6.2.4 Effort; 6.3 Summary; Part II: Data Mining:
A Technical Tutorial; Chapter 7: Data Mining and SE; 7.1
Some Definitions; 7.2 Some Application Areas; Chapter 8:
Defect Prediction.
505 8 8.1 Defect Detection Economics8.2 Static Code Defect
Prediction; 8.2.1 Easy to Use; 8.2.2 Widely Used; 8.2.3
Useful; Chapter 9: Effort Estimation; 9.1 The Estimation
Problem; 9.2 How to Make Estimates; 9.2.1 Expert-Based
Estimation; 9.2.2 Model-Based Estimation; 9.2.3 Hybrid
Methods; Chapter 10: Data Mining (Under the Hood); 10.1
Data Carving; 10.2 About the Data; 10.3 Cohen Pruning;
10.4 Discretization; 10.4.1 Other Discretization Methods;
10.5 Column Pruning; 10.6 Row Pruning; 10.7 Cluster
Pruning; 10.7.1 Advantages of Prototypes; 10.7.2
Advantages of Clustering; 10.8 Contrast Pruning.
505 8 10.9 Goal Pruning10.10 Extensions for Continuous Classes;
10.10.1 How RTs Work; 10.10.2 Creating Splits for
Categorical Input Features; 10.10.3 Splits on Numeric
Input Features; 10.10.4 Termination Condition and
Predictions; 10.10.5 Potential Advantages of RTs for
Software Effort Estimation; 10.10.6 Predictions for
Multiple Numeric Goals; Part III: Sharing Data; Chapter 11
: Sharing Data: Challenges and Methods; 11.1 Houston, We
Have a Problem; 11.2 Good News, Everyone; Chapter 12:
Learning Contexts; 12.1 Background; 12.2 Manual Methods
for Contextualization; 12.3 Automatic Methods.
520 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.
588 0 Online resource; title from title page (Safari, viewed
January 28, 2015).
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Software engineering.
650 0 Data structures (Computer science)
650 6 Génie logiciel.
650 6 Structures de données (Informatique)
650 7 Data structures (Computer science)|2fast
650 7 Software engineering|2fast
700 1 Kocaguneli, Ekrem,|eauthor.
700 1 Turhan, Burak,|eauthor.
700 1 Minku, Leandro,|eauthor.
700 1 Peters, Fayola,|eauthor.
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9780124172951/?ar
|zAvailable on O'Reilly for Public Libraries
994 92|bJFN