Handbook on Artificial Intelligence-Empowered Applied Software Engineering: Volume 1: Novel Methodologies to Engineering Smart Software Systems

Foreword
Preface
Contents
1 Introduction to Handbook on Artificial Intelligence-Empowered Applied Software Engineering—VOL.1: Novel Methodologies to Engineering Smart Software Systems
	1.1 Editorial Note
	1.2 Book Summary and Future Volumes
	Bibliography for Further Reading
Part I Survey of Recent Relevant Literature
2 Synergies Between Artificial Intelligence and Software Engineering: Evolution and Trends
	2.1 Introduction
	2.2 Methodology
	2.3 The Evolution of AI in Software Engineering
	2.4 Top Authors and Topics
	2.5 Trends in AI Applications to Software Engineering
		2.5.1 Machine Learning and Data Mining
		2.5.2 Knowledge Representation and Reasoning
		2.5.3 Search and Optimisation
		2.5.4 Communication and Perception
		2.5.5 Cross-Disciplinary Topics
	2.6 AI-Based Tools
	2.7 Conclusion
	References
Part II Artificial Intelligence-Assisted Software Development
3 Towards Software Co-Engineering by AI and Developers
	3.1 Introduction
	3.2 Software Development Support and Automation Level by Machine Learning
		3.2.1 Project Planning: Team Composition
		3.2.2 Requirements Engineering: Data-Driven Persona
		3.2.3 Design: Detection of Design Patterns
		3.2.4 Categorization of Initiative and Level of Automation
	3.3 Quality of AI Application Systems and Software
		3.3.1 Metamorphic Testing
		3.3.2 Improving Explainability
		3.3.3 Systems and Software Architecture
		3.3.4 Integration of Goals, Strategies, and Data
	3.4 Towards Software Co-Engineering by AI and Developers
	3.5 Conclusion
	References
4 Generalizing Software Defect Estimation Using Size and Two Interaction Variables
	4.1 Introduction
	4.2 Background
	4.3 A Proposed Approach
		4.3.1 Selection of Sample Projects
		4.3.2 Data Collection
		4.3.3 The Scope and Decision to Go with ‘Interaction’ Variables
		4.3.4 Data Analysis and Results Discussion
		4.3.5 The Turning Point
		4.3.6 Models Performance—Outside Sample
	4.4 Conclusion and Limitations
	4.5 Future Research Directions
	4.6 Annexure—Model Work/Details
	References
5 Building of an Application Reviews Classifier by BERT and Its Evaluation
	5.1 Background
	5.2 The Process of Building a Machine Learning Model
	5.3 Dataset
	5.4 Preprocessing
	5.5 Feature Engineering
		5.5.1 Bag of Words (BoW) [4]
		5.5.2 FastText [5, 6]
		5.5.3 Bidirectional Encoder Representations from Transformers (BERT) Embedding [7]
	5.6 Machine-Learning Algorithms
		5.6.1 Naive Bayes
		5.6.2 Logistic Regression
		5.6.3 BERT
	5.7 Training and Evaluation Methods
	5.8 Results
	5.9 Discussion
		5.9.1 Comparison of Classifier Performances
		5.9.2 Performance of the Naive Bayes Classifiers
		5.9.3 Performance of the Logistic Regression Classifiers
		5.9.4 Visualization of Classifier Attention Using the BERT
	5.10 Threats to Validity
		5.10.1 Labeling Dataset
		5.10.2 Parameter Tuning
	5.11 Summary
	References
6 Harmony Search-Enhanced Software Architecture Reconstruction
	6.1 Introduction
	6.2 Related Work
	6.3 HS Enhanced SAR
		6.3.1 SAR Problem
		6.3.2 HS Algorithm
		6.3.3 Proposed Approach
	6.4 Experimentation
		6.4.1 Test Problems
		6.4.2 Competitor approaches
	6.5 Results and Discussion
	6.6 Conclusion and Future Work
	References
7 Enterprise Architecture-Based Project Model for AI Service System Development
	7.1 Introduction
	7.2 Related Work
	7.3 AI Servie System and Enterprise Architecture
		7.3.1 AI Service System
		7.3.2 Enterprise Architecture and AI Service System
	7.4 Modeling Business IT Alignment for AI Service System
		7.4.1 Generic Business–AI Alignment Model
		7.4.2 Comparison with Project Canvas Model
	7.5 Business Analysis Method for Constructing Domain Specific Business–AI Alignment Model
		7.5.1 Business Analysis Tables
		7.5.2 Model Construction Method
	7.6 Practice
		7.6.1 Subject Project
		7.6.2 Result
	7.7 Discussion
	7.8 Conclusion
	References
Part III Software Engineering Tools to Develop Artificial Intelligence Applications
8 Requirements Engineering Processes for Multi-agent Systems
	8.1 Introduction
	8.2 Background
		8.2.1 Agents, Multiagent Systems, and the BDI Model
		8.2.2 Requirements Engineering
	8.3 Techniques and Process of Requirements Engineering for Multiagent Systems
		8.3.1 Elicitation Requirements Techniques for Multiagent Systems
		8.3.2 Requirements Engineering Processes for Multiagent Systems
		8.3.3 Requirements Validation
	8.4 Conclusion
	References
9 Specific UML-Derived Languages for Modeling Multi-agent Systems
	9.1 Introduction
	9.2 Backgroud
		9.2.1 UML
		9.2.2 Agents, Multiagent Systems, and the BDI Model
		9.2.3 BDI Models
	9.3 AUML—Agent UML
	9.4 AORML—Agent-Object-Relationship Modeling Language
		9.4.1 Considerations About AORML
	9.5 AML—Agent Modeling Language
		9.5.1 Considerations About AML
	9.6 MAS-ML—Multiagent System Modeling Language
		9.6.1 Considerations About MAS-ML
	9.7 SEA-ML—Semantic Web Enabled Agent Modeling Language
		9.7.1 Considerations
	9.8 MASRML—A Domain-Specific Modeling Language for Multi-agent Systems Requirements
		9.8.1 Considerations
	References
10 Methods for Ensuring the Overall Safety of Machine Learning Systems
	10.1 Introduction
	10.2 Related Work
		10.2.1 Safety of Machine Learning Systems
		10.2.2 Conventional Safety Model
		10.2.3 STAMP and Its Related Methods
		10.2.4 Standards for Software Lifecycle Processes and System Lifecycle Processes
		10.2.5 Social Technology Systems and Software Engineering
		10.2.6 Software Layer Architecture
		10.2.7 Assurance Case
		10.2.8 Autonomous Driving
	10.3 Safety Issues in Machine Learning Systems
		10.3.1 Eleven Reasons Why We Cannot Release Autonomous Driving Cars
		10.3.2 Elicitation Method
		10.3.3 Eleven Problems on Safety Assessment for Autonomous Driving Car Products
		10.3.4 Validity to Threats
		10.3.5 Safety Issues of Automatic Operation
		10.3.6 Task Classification
		10.3.7 Unclear Assurance Scope
		10.3.8 Safety Assurance of the Entire System
		10.3.9 Machine Learning and Systems
	10.4 STAMP S&S Method
		10.4.1 Significance of Layered Modeling of Complex Systems
		10.4.2 STAMP S&S and Five Layers
		10.4.3 Scenario
		10.4.4 Specification and Standard
	10.5 CC-Case
		10.5.1 Definition of CC-Case
		10.5.2 Technical Elements of CC-Case
	10.6 Measures for Autonomous Driving
		10.6.1 Relationship Between Issues and Measures Shown in This Section
		10.6.2 Measure 1: Analyze Various Quality Attributes in Control Action Units
		10.6.3 Measure 2: Modeling the Entire System
		10.6.4 Measure 3: Scenario Analysis and Specification
		10.6.5 Measure 4: Socio-Technical System
	10.7 Considerations in Level 3 Autonomous Driving
		10.7.1 Example of Autonomous Driving with the 5-layered Model of STAMP S&S
	10.8 Conclusion
	References
11 MEAU: A Method for the Evaluation of the Artificial Unintelligence
	11.1 Introduction
	11.2 Machine Learning and Online Unintelligence: Improvisation or Programming?
	11.3 The New Paradigm of Information from Digital Media and Social Networks
	11.4 Numbers, Images and Texts: Sources of Errors, Misinformation and Unintelligence
	11.5 MEAU: A Method for the Evaluation of the Artificial Unintelligence
	11.6 Results
	11.7 Lessons Learned
	11.8 Conclusions
	Appendix 1
	Appendix 2
	Appendix 3
	Appendix 4
	References
12 Quantum Computing Meets Artificial Intelligence: Innovations and Challenges
	12.1 Introduction
		12.1.1 Benefits of Quantum Computing for AI
	12.2 Quantum Computing Motivations
		12.2.1 What Does ``Quantum'' Mean?
		12.2.2 The Wave-Particle Duality
		12.2.3 Qubit Definition
		12.2.4 The Schrödinger Equation
		12.2.5 Superposition
		12.2.6 Interference
		12.2.7 Entanglement
		12.2.8  Gate-Based Quantum Computing
	12.3 Quantum Machine Learning
		12.3.1 Variational Quantum Algorithms
		12.3.2 Data Encoding
		12.3.3 Quantum Neural Networks
		12.3.4 Quantum Support Vector Machine
		12.3.5 Variational Quantum Generator
	12.4 Quantum Computing Limitations and Challenges
		12.4.1 Scalability and Connectivity
		12.4.2 Decoherence
		12.4.3 Error Correction
		12.4.4 Qubit Control
	12.5 Quantum AI Software Engineering
		12.5.1 Hybrid Quantum-Classical Frameworks
		12.5.2 Friction-Less Development Environment
		12.5.3 Quantum AI Software Life Cycle
	12.6 A new Problem Solving Approach
		12.6.1 Use Case 1: Automation and Transportation Sector
		12.6.2 Use Case 2: Food for the Future World
		12.6.3 Use Case 3: Cheaper Reliable Batteries
		12.6.4 Use Case 4: Cleaner Air to Breathe
		12.6.5 Use Case 5: AI-Driven Financial Solutions
	12.7 Summary and Conclusion
	References