System Reliability and Security: Techniques and Methodologies

Cover
Half Title
Title Page
Copyright Page
Table of Contents
List of Contributors
1 GNN Approach for Software Reliability
	Acronyms With Definitions
	1.1 Introduction
	1.2 Software Defect Prediction Approaches
		1.2.1 Traditional SDP Techniques
		1.2.2 Deep Learning in SDP
		1.2.3 Summary
	1.3 Understanding the Structure of a Software Program as Graph
		1.3.1 Abstract Syntax Tree
		1.3.2 Function Call Graph
		1.3.3 Data Flow Graph
	1.4 GNN Approach for Defect Rate Prediction Using Graph Structure of a Program
		1.4.1 GNN Architecture
			1.4.1.1 Input Layer
			1.4.1.2 GNN Layers
			1.4.1.3 Output Layer
		1.4.2 Applying GNN to AST
	1.5 Conclusion
	References
2 Software Reliability Prediction Using Neural Networks: A Non-Parametric Approach
	2.1 Introduction
	2.2 Approaches for Software Reliability Modeling
		2.2.1 Parametric Software Reliability Growth Models
			2.2.1.1 Yamada Delayed S-Shaped Model
			2.2.1.2 Goel–Okumoto Model
			2.2.1.3 Generalized Goel NHPP Mode
			2.2.1.4 Logistic Growth Curve Model
			2.2.1.5 MO Model
			2.2.1.6 Pham–Nordmann–Zhang (PNZ) Model
			2.2.1.7 Pham–Zhang (P-Z) Model
			2.2.1.8 Yamada Imperfect Debugging Model 1
			2.2.1.9 Yamada Imperfect Debugging Model 2
		2.2.2 Non-Parametric Reliability Growth Models
	2.3 Software Reliability
		2.3.1 Software Reliability Measures
		2.3.2 Parameter Estimation Techniques
		2.3.3 Failure Data Sets
	2.4 ANN Approach for Reliability
	2.5 Conclusion
	References
3 Analysis and Modeling of Software Reliability Using Deep Learning Methods
	3.1 Introduction
	3.2 Related Work
		3.2.1 Novel Deep Learning Solutions for Software Defect Detection and Reliability
		3.2.2 Transformers as a Novel Proposed Solution
			3.2.2.1 Introduction to Word Embedding and Word2vec
			3.2.2.2 Transformer Deep Learning Model
	3.3 Conclusion
	References
4 Fixed-Design Local Polynomial Approach for Estimation and Hypothesis Testing of Reliability Measures
	4.1 Introduction
	4.2 Popular Component Reliability Measures
		4.2.1 Empirical Estimators
		4.2.2 Fixed-Design Local Polynomial Estimators
			4.2.2.1 Fixed-Design Local Polynomial Estimators: Asymptotic Properties
			4.2.2.2 Dealing With a Randomly Censored Dataset
			4.2.2.3 Optimal Bin Width and Bandwidth Selection
		4.2.3 Performance of Proposed Estimators
	4.3 Non-Parametric Hypothesis Tests for Comparing Reliability Functions
		4.3.1 Statistical Comparison of Expected Inactivity Time Functions of Two Populations
			4.3.1.1 Critical Values of the Test Statistics
		4.3.2 Statistical Comparison of Mean Residual Life Functions of Two Populations
			4.3.2.1 Critical Values of the Test Statistics
			4.3.2.2 Using Bootstrapping to Calculate the Critical Value
		4.3.3 Evaluating Efficiency of the Proposed Hypothesis Tests
		4.3.4 Practical Performance
	4.4 Conclusion
	References
5 Reliability Analysis of Relation Between Urbanization, Vegetation Health, and Heat Island Through Markov Chain Model
	5.1 Introduction
	5.2 Materials and Methods
		5.2.1 Normalized Difference Vegetation Index (NDVI)
		5.2.2 Normalized Difference Built-Up Index (NDBI)
		5.2.3 Land Surface Temperature Method
		5.2.4 Analytical Hierarchy Process (AHP)
		5.2.5 Markov Chain Model
			5.2.5.1 Governmental Model
		5.2.6 Finding Supportive Policy
	5.3 Result and Discussion
		5.3.1 Temporal Analysis of Land Use and Land Cover of Kolkata Municipal Area
		5.3.2 Temporal Analysis of Normalized Difference Vegetation Index (NDVI) of Kolkata Municipal Area
		5.3.3 Temporal Analysis of Normalized Difference Built-Up Index (NDBI) of Kolkata Municipal Area
		5.3.4 Scenario of Urban Heat Island of Kolkata Municipal Area From 1999 to 2022
		5.3.5 Analytical Hierarchy Process
		5.3.6 Markov Chain
	5.4 Conclusion
	References
6 Modeling and IoT (Internet of Things) Analysis for Smart Precision Agriculture
	6.1 Introduction
		6.1.1 How IoT in Agriculture Has Left Its Mark
		6.1.2 Application of IoT in Agriculture
		6.1.3 Environmental Factors
		6.1.4 Precision Farming
		6.1.5 Smart Greenhouses
		6.1.6 Data Analytics
		6.1.7 Agricultural Drones
	6.2 Related Work
		6.2.1 User-Centered Design Models
		6.2.2 Internet of Things: Protocols and Architectures
		6.2.3 Internet of Things Technologies Applied On PA Scenarios
		6.2.4 Edge and Fog Computing Paradigms: Evolution of the Internet of Things, Cloud, and Machine Learning
		6.2.5 Automated Greenhouse Technologies
	6.3 Materials and Methods
		6.3.1 User-Centered Analysis and Design
		6.3.2 Data Analysis: Configuration of Edge and Fog Computing
		6.3.3 Things and Communication
		6.3.4 Network Platform: Development and Design
		6.3.5 Platform Development
	6.4 Conclusions and Future Work
	References
7 Engineering Challenges in the Development of Artificial Intelligence and Machine Learning Software Systems
	7.1 Introduction
	7.2 Categories of Challenges in AI/ML Software Systems
		7.2.1 Software Testing and Quality Assurance
		7.2.2 Model Development
		7.2.3 Project Management and Infrastructure
		7.2.4 Requirement Engineering
		7.2.5 Architecture Design and Integration
		7.2.6 Model Deployment
		7.2.7 Engineering
	7.3 Summary
	References
8 Study and Analysis of Testing Effort Functions for Software Reliability Modeling
	8.1 Introduction
	8.2 Summary of Some Famous TEFs Used in the Literature
	8.3 Numerical Analysis of 12 TEFs Employed in This Study
	8.4 Numerical Analysis
	8.5 Conclusion
	Acknowledgment
	References
9 Summary of NHPP-Based Software Reliability Modeling With Lindley-Type Distributions
	9.1 Introduction
	9.2 NHPP-Based Software Reliability Modeling
		9.2.1 Finite-Failure (Finite) NHPP-Based SRMs
		9.2.2 Infinite-Failure (Infinite) NHPP-Based SRMs
	9.3 Lindley-Type Distribution
	9.4 Maximum Likelihood Estimation
	9.5 Performance Illustration
		9.5.1 Goodness-Of-Fit Performance
		9.5.2 Predictive Performance
		9.5.3 Software Reliability Assessment
	9.6 Conclusion
	References
10 Artificial Intelligence and Machine Learning Problems and Challenges in Software Testing
	10.1 Introduction
		10.1.1 Overview of Machine Learning and Artificial Intelligence
		10.1.2 Impact of AI On Software Testing
		10.1.3 Role of AI in Software Testing
	10.2 Issues and Challenges of AI
		10.2.1 Recognizing Test Data
		10.2.2 Algorithmic Uncertainty
		10.2.3 Measures of Effectiveness That Are Not Accurate
		10.2.4 Data Splitting Into Training and Testing Sets
	10.3 Related Work
		10.3.1 Artificial Intelligence Overview
		10.3.2 Artificial Neural Network
		10.3.3 AI Planning
		10.3.4 Machine Learning
		10.3.5 Natural Language Processing (NLP)
		10.3.6 Fuzzy Logic
	10.4 Artificial Intelligence in Agriculture
		10.4.1 Software Testing in the Area of Artificial Intelligence for Agriculture
		10.4.2 Development Spurred By the Internet of Things (IoT)
	10.5 Software Testing Overview
	10.6 Tools
		10.6.1 Testim.io
		10.6.2 Appvance
		10.6.3 Test.ai
		10.6.4 Functioned
	10.7 Conclusion
	10.8 Future Work
	References
11 Software Quality Prediction By CatBoost Feed-Forward Neural Network in Software Engineering
	11.1 Introduction
	11.2 Literature Review
		11.2.1 Parameters That Influence Software Quality
			11.2.1.1 Software Efficiency
			11.2.1.2 Mode of Software Development
			11.2.1.3 Developer Or Developer Team
		11.2.2 Machine Learning Framework
		11.2.3 Analysis With Respect to Existing Work
	11.3 Methodology Or Framework
		11.3.1 Exploratory Analysis
		11.3.2 Data Preprocessing
		11.3.3 Feature Engineering
		11.3.4 Training and Testing the Model
		11.3.5 Evaluation
	11.4 Results
	11.5 Conclusion
	References
12 Software Security
	12.1 Introduction
		12.1.1 Software Security Process
	12.2 Challenges and Requirements
		12.2.1 Security Requirements Modeling
		12.2.2 Validation Requirements Modeling
	12.3 Software Security Vulnerabilities
		12.3.1 Security Automation in Software-Defined Networks
		12.3.2 Security Threat-Oriented Requirements Engineering Methodology
	12.4 Environment and System Security
		12.4.1 Levels of Security
		12.4.2 Level I—Minimal Protection
		12.4.3 Level II—Advanced Protection
		12.4.4 Level III—Maximal Protection
	12.5 Cloud Security
		12.5.1 Infrastructure-As-A-Service (IaaS) and Platform-As-A-Service (PaaS)
		12.5.2 Software-As-A-Service (SaaS)
		12.5.3 Software Testing Metrics
	12.6 Conclusion
	References
13 Definitive Guide to Software Security Metrics
	13.1 Introduction
	13.2 Related Work
	13.3 Software Security Measurement Primer
	13.4 Security Metrics Taxonomies
	13.5 Conclusion
	References
14 Real-Time Supervisory Control and Data Acquisition (SCADA) Model for Resourceful Distribution and Use of Public Water
	14.1 Introduction
	14.2 Stage 1 (Automatic Water Pumping)
	14.3 Stage 2 (Automatic Water Distribution in the City)
	14.4 Stage 3 (Automatic Water Leakage Detection System)
	14.5 Stage 4 (Pressure Or Storage Tank)
	14.6 System Simulation Environment
	14.7 Programmable Logic Controllers (PLCs)
	14.8 Field Instruments
	14.9 SCADA Software
	14.10 InTouch Application Manager
	14.11 Human–Machine Interface (HMI)
	14.12 Research Tools and Techniques
		14.12.1 A. Automatic Pumping of Water From Well
	14.13 Automatic Water Pumping
		14.13.1 B. Automatic Water Distribution System in the City
	14.14 Automatic Water Distribution
		14.14.1 C. Automatic Water Leakage Detection System
	14.15 Water Leakage System
		14.15.1 D. Automatic Water Pumping System Using SCADA
	14.16 Automatic Water Pumping System
	14.17 Storage Water Pumping
	14.18 Conclusion
	References
Index