Sharing Data and Models in Software Engineering

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

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

Content: Front Matter, Pages i-ii Copyright, Page iv Why this book?, Page v Foreword, Pages vii-viii List of Figures☆, Pages xix-xxvii Chapter 1 - Introduction, Pages 1-14 Chapter 2 - Rules for Managers, Pages 17-18 Chapter 3 - Rule #1: Talk to the Users, Pages 19-23 Chapter 4 - Rule #2: Know The Domain, Pages 25-28 Chapter 5 - Rule #3: Suspect Your Data, Pages 29-34 Chapter 6 - Rule #4: Data Science is Cyclic, Pages 35-38 Chapter 7 - Data Mining and SE, Pages 41-42 Chapter 8 - Defect Prediction, Pages 43-46 Chapter 9 - Effort Estimation, Pages 47-50 Chapter 10 - Data Mining (Under The Hood), Pages 51-75 Chapter 11 - Sharing Data: Challenges and Methods, Pages 79-81 Chapter 12 - Learning Contexts, Pages 83-100 Chapter 13 - Cross-Company Learning: Handling The Data Drought, Pages 101-124 Chapter 14 - Building Smarter Transfer Learners, Pages 125-146 Chapter 15 - Sharing Less Data (Is a Good Thing), Pages 147-164 Chapter 16 - How To Keep Your Data Private, Pages 165-196 Chapter 17 - Compensating for Missing Data, Pages 197-211 Chapter 18 - Active Learning: Learning More With Less, Pages 213-234 Chapter 19 - Sharing Models: Challenges and Methods, Page 237 Chapter 20 - Ensembles of Learning Machines, Pages 239-265 Chapter 21 - How to Adapt Models in a Dynamic World, Pages 267-290 Chapter 22 - Complexity: Using Assemblies of Multiple Models, Pages 291-304 Chapter 23 - The Importance of Goals in Model-Based Reasoning, Pages 305-320 Chapter 24 - Using Goals in Model-Based Reasoning, Pages 321-353 Chapter 25 - A Final Word, Pages 355-356 Bibliography, Pages 357-378 __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.