Intro
Foreword
Preface
Acknowledgment
Contents
Contributors
1 Introduction
1.1 Introduction
1.2 The Rise of Artificial Intelligence
1.3 AI4SE: Artificial Intelligence for Software Engineering
1.4 Organisation of This Book
1.4.1 AI for the Software Development Process
1.4.2 Background on Metaheuristics and Machine Learning
References
Part I Planning and Analysis
2 Artificial Intelligence in Software Project Management
2.1 Introduction
2.2 Software Project Scheduling (SPS)
2.2.1 AI Approaches for SPS
2.2.2 Running an AI Algorithm for SPS

2.3 Software Effort Estimation (SEE)
2.3.1 AI Approaches for SEE
2.3.2 Running an AI Algorithm for SEE
2.4 Conclusion
References
3 Requirements Engineering
3.1 Introduction
3.2 Requirements Engineering
3.3 Use Case About Requirements Elicitation
3.3.1 The Problem of Requirements Elicitation from User Feedback
3.3.2 A Solution to Requirements Elicitation Based on NLP Techniques
3.3.3 Identifying Speech Acts
3.3.4 Training a Classifier
3.3.5 Applying on Two Case Studies
3.3.6 Discussion
3.4 Use Case About Requirements Prioritisation

3.4.1 The Problem of Requirements Prioritisation Using User Feedback
3.4.2 A Solution to Requirements Prioritisation Based on Genetic Algorithms
3.4.3 Applying the Prioritisation Method
3.4.4 Discussion
3.5 The Two Use Cases in a Requirements Management Process
3.6 Discussion
3.7 Conclusions
References
4 Leveraging Artificial Intelligence for Model-based Software Analysis and Design
4.1 Introduction
4.2 Background
4.2.1 Model-Driven Engineering: Models, Meta-models, and Model Transformations
4.2.2 Running Example

4.2.3 Selected Applications of AI for Model-Based Engineering Problems
4.3 Optimizing Models with AI Techniques: Two Encodings for the Modularization Case
4.3.1 Model-based versus Transformation-based Encodings: An Overview
4.3.2 Model-based Approach
4.3.3 Transformation-based Approach
4.3.4 Synopsis
4.4 Conclusion and Outlook
References
Part II Development and Deployment
5 Statistical Models and Machine Learning to Advance Code Completion: Are We There Yet?
5.1 Introduction
5.2 Code Completion with Software Mining
5.2.1 Frequent Pairs or Sets as Code Patterns

5.2.2 Graphs of Code Elements as Code Patterns
5.2.3 Leveraging Editing History for Code Completion
5.2.4 API Code Recommendation Using Statistical Learning from Fine-Grained Changes
5.3 Code Completion with Statistical Language Models
5.3.1 N-Gram Language Model
5.3.2 Lexical Code Tokens and Sequences
5.3.3 Lexical N-Gram Model for Source Code
5.3.4 Semantic n-Gram Language Model for Source Code
5.3.5 Code Suggestion with Semantic N-Gram Language Model
5.3.6 Graph-Based Statistical Language Model
5.4 Deep Learning for Code Completion