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