Sharing data and models in software engineering / Tim Menzies, Ekrem Kocaguneli, Leandro Minku, Fayola Peters, Burak Turhan.

computer bookfair2016.

Includes bibliographic references.

Chapter 1: Introduction
Part I: Data Mining for Managers
Chapter 2: Rules for Managers
Chapter 3: Rule #1: Talk to the Users
Chapter 4: Rule #2: Know The Domain
Chapter 5: Rule #3: Suspect Your Data
Chapter 6: Rule #4: Data Science is Cyclic
Chapter 6: Rule #4: Data Science is Cyclic
Part II: Data Mining: A Technical Tutorial
Chapter 7: Data Mining and SE
Chapter 8: Defect Prediction
Chapter 9: Effort Estimation
Chapter 10: Data Mining (Under The Hood)
Part III: Sharing Data
Chapter 11: Sharing Data: Challenges and Methods
Chapter 12: Learning Contexts
Chapter 13: Cross-Company Learning: Handling The Data Drought
Chapter 14: Building Smarter Transfer Learners
Chapter 15: Sharing Less Data (Is a Good Thing)
Chapter 16: How To Keep Your Data Private
Chapter 17: Compensating for Missing Data
Chapter 18: Active Learning: Learning More With Less
Part IV: Sharing Models
Chapter 19: Sharing Models: Challenges and Methods
Chapter 20: Ensembles of Learning Machines
Chapter 21: How to Adapt Models in a Dynamic World
Chapter 22: Complexity: Using Assemblies of Multiple Models
Chapter 23: The Importance of Goals in Model-Based Reasoning
Chapter 24: Using Goals in Model-Based Reasoning
Chapter 25: A Final Word

Available to OhioLINK libraries.