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Part III: Sharing Data
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Part III: Sharing Data
by Fayola Peters, Leandro Minku, Burak Turhan, Ekrem Kocaguneli, Tim Menzies
Sharing Data and Models in Software Engineering
Cover image
Title page
Table of Contents
Copyright
Why this book?
Foreword
List of Figures
Chapter 1: Introduction
1.1 Why read this book?
1.2 What do we mean by “sharing”?
1.3 What? (our executive summary)
1.4 How to read this book
1.5 But what about …? (what is not in this book)
1.6 Who? (about the authors)
1.7 Who else? (acknowledgments)
Part I: Data Mining for Managers
Chapter 2: Rules for Managers
Abstract
2.1 The inductive engineering manifesto
2.2 More rules
Chapter 3: Rule #1: Talk to the Users
Abstract
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
Abstract
4.1 Cautionary tale #1: “discovering” random noise
4.2 Cautionary tale #2: jumping at shadows
4.3 Cautionary tale #3: it pays to ask
4.4 Summary
Chapter 5: Rule #3: Suspect Your Data
Abstract
5.1 Controlling Data Collection
5.2 Problems With Controlled Data Collection
5.3 Rinse (and Prune) Before Use
5.4 On the Value of Pruning
5.5 Summary
Chapter 6: Rule #4: Data Science is Cyclic
Abstract
6.1 The Knowledge Discovery Cycle
6.2 Evolving Cyclic Development
6.3 Summary
Part II: Data Mining: A Technical Tutorial
Chapter 7: Data Mining and SE
Abstract
7.1 Some Definitions
7.2 Some Application Areas
Chapter 8: Defect Prediction
Abstract
8.1 Defect Detection Economics
8.2 Static Code Defect Prediction
Chapter 9: Effort Estimation
Abstract
9.1 The Estimation Problem
9.2 How To Make Estimates
Chapter 10: Data Mining (Under The Hood)
Abstract
10.1 Data carving
10.2 About the data
10.3 Cohen pruning
10.4 Discretization
10.5 Column pruning
10.6 Row pruning
10.7 Cluster pruning
10.8 Contrast pruning
10.9 Goal pruning
10.10 Extensions for continuous classes
Part III: Sharing Data
Chapter 11: Sharing Data: Challenges and Methods
Abstract
11.1 Houston, We Have A Problem
11.2 Good News, Everyone
Chapter 12: Learning Contexts
Abstract
12.1 Background
12.2 Manual Methods for Contextualization
12.3 Automatic Methods
12.4 Other Motivation To Find Contexts
12.5 How To Find Local Regions
12.6 Inside Chunk
12.7 Putting It All Together
12.8 Using Chunk
12.9 Closing Remarks
Chapter 13: Cross-Company Learning: Handling The Data Drought
Abstract
13.1 Motivation
13.2 Setting the ground for analyses
13.3 Analysis #1: can CC data be useful for an organization?
13.4 Analysis #2: how to cleanup CC data for local tuning?
13.5 Analysis #3: how much local data does an organization need for a local model?
13.6 How trustworthy are these results?
13.7 Are these useful in practice or just number crunching?
13.8 What's new on cross-learning?
13.9 What's the takeaway?
Chapter 14: Building Smarter Transfer Learners
Abstract
14.1 What is actually the problem?
14.2 What do we know so far?
14.3 An example technology: TEAK
14.4 The details of the experiments
14.5 Results
14.6 Discussion
14.7 What are the takeaways?
Chapter 15: Sharing Less Data (Is a Good Thing)
Abstract
15.1 Can We Share Less Data?
15.2 Using Less Data
15.3 Why Share Less Data?
15.4 How To Find Less Data
15.5 What's Next?
Chapter 16: How To Keep Your Data Private
Abstract
16.1 Motivation
16.2 What is PPDP and why is it important?
16.3 What is considered a breach of privacy?
16.4 How to avoid privacy breaches?
16.5 How are privacy-preserving algorithms evaluated?
16.6 Case study: privacy and cross-company defect prediction
Chapter 17: Compensating for Missing Data
Abstract
17.1 Background notes on see and instance selection
17.2 Data sets and performance measures
17.3 Experimental conditions
17.4 Results
17.5 Summary
Chapter 18: Active Learning: Learning More With Less
Abstract
18.1 How does the quick algorithm work?
18.2 Notes on active learning
18.3 The application and implementation details of quick
18.4 How the experiments are designed
18.5 Results
18.6 Summary
Part IV: Sharing Models
Chapter 19: Sharing Models: Challenges and Methods
Abstract
Chapter 20: Ensembles of Learning Machines
Abstract
20.1 When and why ensembles work
20.2 Bootstrap aggregating (bagging)
20.3 Regression trees (RTs) for bagging
20.4 Evaluation framework
20.5 Evaluation of bagging + RTs in SEE
20.6 Further understanding of bagging + RTs in SEE
20.7 Summary
Chapter 21: How to Adapt Models in a Dynamic World
Abstract
21.1 Cross-company data and questions tackled
21.2 Related work
21.3 Formulation of the problem
21.4 Databases
21.5 Potential benefit of CC data
21.6 Making better use of CC data
21.7 Experimental analysis
21.8 Discussion and implications
21.9 Summary
Chapter 22: Complexity: Using Assemblies of Multiple Models
Abstract
22.1 Ensemble of methods
22.2 Solo methods and multimethods
22.2.3 Experimental conditions
22.3 Methodology
22.4 Results
22.5 Summary
Chapter 23: The Importance of Goals in Model-Based Reasoning
Abstract
23.1 Introduction
23.2 Value-based modeling
23.3 Setting up
23.4 Details
23.5 An experiment
23.6 Inside the models
23.7 Results
23.8 Discussion
Chapter 24: Using Goals in Model-Based Reasoning
Abstract
24.1 Multilayer Perceptrons
24.2 Multiobjective evolutionary algorithms
24.3 HaD-MOEA
24.4 Using MOEAs for creating see models
24.5 Experimental setup
24.6 The relationship among different performance measures
24.7 Ensembles based on concurrent optimization of performance measures
24.8 Emphasizing particular performance measures
24.9 Further analysis of the model choice
24.10 Comparison against other types of models
24.11 Summary
Chapter 25: A Final Word
Abstract
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Chapter 10: Data Mining (Under The Hood)
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Chapter 11: Sharing Data: Challenges and Methods
Part III
Sharing Data
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