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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Morgan Kaufmann,
[2015]
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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''?
- 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.
- 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.
- 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.
- 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.