Agile Software Development: Trends, Challenges and Applications

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 Agile Software Development in the Digital World – Trends and Challenges
	1.1 Introduction
		1.1.1 Organization of Chapter
	1.2 Related Work
		1.2.1 Teamwork Development
		1.2.2 Project-Based Learning (PJBL)
		1.2.3 Planning the Agile Software Development Methodologies
	1.3 Agile Architecture Trends in the Digital World
		1.3.1 Agile Implementation at Scale
	1.4 Challenges Faced in the Digital World Through Agile Software Development
		1.4.1 Challenges for Small to Mid-Scale and Large-Scale Agile Projects
		1.4.2 Reported Challenges – Cause and Potential Solutions
	1.5 Generic Guidelines to Improve the Agile Transformation in Digital World
	1.6 Conclusion and Future Perspective
	References
Chapter 2 Agile Framework Adaptation Issues in Various Sectors
	2.1 Introduction
		2.1.1 Human-Human Linkages
	2.2 Agile Followers
	2.3 Proposed Work
	2.4 Resolution Matrix
	2.5 Conclusion and Future Work
	References
Chapter 3 Vulnerability Assessment Tools for IoT: An Agile Approach
	3.1 Introduction
	3.2 Agile Methodology: SCRUM
	3.3 Scrum Agile Benefits for IoT
	3.4 Critical Factors for Implementing Agile Methodology
	3.5 Conclusion
	References
Chapter 4 Interoperable Agile IoT
	4.1 Introduction
	4.2 Agile Software Development
		4.2.1 Scrum Methodology
		4.2.2 Extreme Programming (XP)
		4.2.3 Adaptive Software Development (ASD)
		4.2.4 Dynamic Software Development Method (DSDM)
		4.2.5 Feature Driven Development (FDD)
		4.2.6 Kanban Method
	4.3 Internet of Things (IoT)
	4.4 Agile–IoT Project for Interoperability
	4.5 Agile–IoT Project for Smart Domains
	4.6 INTER-IoT Framework for Interoperability
		4.6.1 Interoperability Aspects
	4.7 Conclusion
	References
Chapter 5 Functional and Non-Functional Requirements in Agile Software Development
	5.1 Introduction
	5.2 Agile Requirements Gathering
	5.3 Types of Requirements
	5.4 Functional Requirement Gathering
	5.5 Non-Functional Requirement Gathering
	5.6 Testing Functional and Non-Functional Requirements
	5.7 Conclusion and Future Scope
	References
Chapter 6 Minimizing Cost, Effort, and Implementation Complexity for Adopting Security Requirements in an Agile Development Process for Cyber-Physical Systems
	6.1 Introduction
	6.2 Literature Review
	6.3 Proposed Methodology
	6.4 Conclusion
	References
Chapter 7 A Systematic Literature Review on Test Case Prioritization Techniques
	7.1 The Motivation for Systematic Review
		7.1.1 Existing Literature Reviews on Test Case Prioritization
		7.1.2 Resources Used for SLR
		7.1.3 Search Criteria
		7.1.4 Research Questions
	7.2 Results
		7.2.1 What is the Current Status of Test Case Prioritization?
		7.2.2 How Various Test Case Prioritization Techniques are Classified? And What are Those Classifications?
			7.2.2.1 Code Coverage-Based
			7.2.2.2 Requirements-Based
			7.2.2.3 Model-Based Prioritization
			7.2.2.4 Time and Cost-Aware Prioritization
			7.2.2.5 History-Based Prioritization
			7.2.2.6 Risk Factor-Based Prioritization
			7.2.2.7 Fault Localization-Based
			7.2.2.8 Soft Computing Techniques-Based
			7.2.2.9 Web-Based
			7.2.2.10 Object Oriented Testing-Based
			7.2.2.11 Similarity-Based
			7.2.2.12 Combinatorial Interaction Testing-Based
			7.2.2.13 Machine Learning-Based
			7.2.2.14 Adaptive Random Testing (ART)-Based
			7.2.2.15 Prioritization for Continuous Integration (CI) and Software Product Lines (SPL)
			7.2.2.16 Hybrid Approaches
			7.2.2.17 Comparative Studies
			7.2.2.18 Surveys and Reviews
	7.3 What Subject Systems Have Been Used to Evaluate Test Case Prioritization Techniques? What is the Type of Programming Platform for Subject Systems?
		7.3.1 What is Research Status in Model-Based Test Case Prioritization?
		7.3.2 What Evaluation Criterion Has Been Used to Evaluate Model-Based Prioritization and How are The Results Reported?
		7.3.3 How Model-Based Test Case Prioritization Has Evolved Over the Years? Which Studies Have Discussed the Benefits of Model-Based Test Case Prioritization in Object-Oriented Systems?
		7.3.4 What Subject Systems Are Used to Evaluate the Model-Based Test Case Prioritization?
		7.3.5 What is the Research Status of Test Case Prioritization for Object-Oriented Testing?
		7.3.6 What Specific Parameters of Object-Oriented Testing Have Been Highlighted by Various Studies?
		7.3.7 What Studies Exist Based on Multi-Objective Algorithms for Test Case Prioritization in Object-Oriented Testing?
		7.3.8 Whether Comparative Analysis of Multi-Objective Algorithms for Test Case Prioritization in Object-Oriented Testing Has Been Performed? And What are The Results?
	7.4 Research Gaps
	References
Chapter 8 A Systematic Review of the Tools and Techniques in Distributed Agile Software Development
	8.1 Introduction
		8.1.1 Why Agile?
		8.1.2 Distributed Agile Software Development (DASD)
		8.1.3 Challenges of DASD
			8.1.3.1 Documentation
			8.1.3.2 Pair Programming
			8.1.3.3 Different Working Hours
			8.1.3.4 Training on Agile Practices
			8.1.3.5 Distribution of Work
	8.2 Literature Review
	8.3 Techniques for DASD
		8.3.1 Effective Communication
		8.3.2 Face Visits or Contact Visits
		8.3.3 Team Distribution
		8.3.4 Distribution of Work
		8.3.5 Documentation
	8.4 Tools for DASD
		8.4.1 Monday.com
			8.4.1.1 Features
			8.4.1.2 Pricing
		8.4.2 nTask
			8.4.2.1 Features
			8.4.2.2 Pricing
		8.4.3 Jira
			8.4.3.1 Pricing
			8.4.3.2 Version Control
			8.4.3.3 Key Features
		8.4.4 ActiveCollab
			8.4.4.1 Pricing
			8.4.4.2 Features
		8.4.5 Pivotal Tracker
			8.4.5.1 Features
			8.4.5.2 Pricing
		8.4.6 Clarizen
			8.4.6.1 Software Features
		8.4.7 Axosoft
			8.4.7.1 Software Features
			8.4.7.2 Pricing
		8.4.8 MeisterTask
			8.4.8.1 Software Features
			8.4.8.2 Pricing
		8.4.9 GitLab
			8.4.9.1 Features
			8.4.9.2 Pricing
		8.4.10 Productboard
			8.4.10.1 Features
		8.4.11 ZohoSprints
			8.4.11.1 Features
			8.4.11.2 Pricing
		8.4.12 Taskworld
			8.4.12.1 Features
			8.4.12.2 Pricing
		8.4.13 CoSchedule
			8.4.13.1 Features
			8.4.13.2 Pricing
		8.4.14 Nostromo
			8.4.14.1 Features
			8.4.14.2 Pricing
		8.4.15 Todo.vu
			8.4.15.1 Features
			8.4.15.2 Pricing
		8.4.16 VersionOne
			8.4.16.1 Pricing
			8.4.16.2 Features
		8.4.17 ProofHub
			8.4.17.1 Features
			8.4.17.2 Pricing
	8.5 Conclusion
	References
Chapter 9 Distributed Agile Software Development (DASD) Process
	9.1 Introduction
	9.2 Distributed Software Development
		9.2.1 Factors Influencing Agile Distributed Software Development
	9.3 Distributed Agile Software Development Team
		9.3.1 Distributed Agile Development/Teams
			9.3.1.1 Some Common Practices for Agile Teams are Specified as Below
	9.4 Scrum in Global Software Development (GSD)
		9.4.1 Aim and Objectives of Scrum Practices in GSD
		9.4.2 Background
		9.4.3 Scrum Practices in GSD
	9.5 Tools and Techniques for Agile Distributed Development
	9.6 Conclusion
	References
Chapter 10 Task Allocation in Agile-Based Distributed Project Development Environment
	10.1 Introduction
		10.1.1 Traditional Software Development
		10.1.2 Agile Software Development (ASD)
		10.1.3 Distributed Software Development
		10.1.4 Motivation and Goal
	10.2 Task Allocation
		10.2.1 Traditional Task Allocation Methods
		10.2.2 Need of Machine Learning in Task Allocation
	10.3 Machine Learning-Based Task Allocation Model
	10.4 Conclusion
	References
Chapter 11 Software Quality Management by Agile Testing
	11.1 Introduction
	11.2 A Brief Introduction to JMeter
	11.3 Review of Literature
	11.4 Performance Testing Using JMeter
	11.5 Proposed Work
	11.6 Results and Discussions
	11.7 Conclusion
	References
Chapter 12 A Deep Drive into Software Development Agile Methodologies for Software Quality Assurance
	12.1 Introduction
	12.2 Background Work
		12.2.1 Factors of Quality Assurance in Agility
	12.3 Understanding Agile Software Methodologies
		12.3.1 Need for Agile Software Methodology Framework
	12.4 Agile Methodology Evaluation Framework
		12.4.1 Extreme Programming (XP)
		12.4.2 Scrum
		12.4.3 Lean Development
		12.4.4 Crystal Methodology
		12.4.5 Kanban Methodology
		12.4.6 Feature Driven Development (FDD) Methodology
		12.4.7 Dynamic System Development Method (DSDM)
	12.5 Agile Software Development – Issues and Challenges
	12.6 Conclusion
	References
Chapter 13 Factors and Techniques for Software Quality Assurance in Agile Software Development
	13.1 Introduction
		13.1.1 Values of the Agile Manifesto
		13.1.2 The Twelve Agile Manifesto Principles
		13.1.3 Agile for Software Quality Assurance
	13.2 Literature Review
	13.3 Agile Factors in Quality Assurance
		13.3.1 Success Factors
		13.3.2 Failure Factors
	13.4 Quality Assurance Techniques
	13.5 Challenges and Limitations of Agile Technology
	13.6 Conclusion and Future Scope
	References
Chapter 14 Classification of Risk Factors in Distributed Agile Software Development Based on User Story
	14.1 Introduction
	14.2 Software Risk Management
		14.2.1 Risk Assessment
	14.3 Literature Review
		14.3.1 Review
		14.3.2 Risk Factors in Distributed Agile Software Development
		14.3.3 Current Challenges
	14.4 User Story-Based Classification of Risk Factors in Distributed Agile Software Development
		14.4.1 User Stories
		14.4.2 Classification of Risk Factors on the Basis of User Story
	14.5 Future Scope
	14.6 Conclusion
	References
Chapter 15 Software Effort Estimation with Machine Learning – A Systematic Literature Review
	15.1 Introduction
	15.2 Method
		15.2.1 Questionnaires for Research
		15.2.2 Search Process
		15.2.3 Criteria for Inclusion and Removal
		15.2.4 Data Gathering
		15.2.5 Analyzing Data
	15.3 Result
		15.3.1 Findings
	15.4 Discussion
		15.4.1 What Kinds of Research are Being Conducted?
		15.4.2 Who is the Research Leader in SLR?
		15.4.3 The Study’s Limitations
	15.5 Conclusion
	15.6 Future Scope
	References
Chapter 16 Improving the Quality of Open Source Software
	16.1 Introduction
	16.2 Literature Review
	16.3 Research Issues
	16.4 Research Method and Data Collection
	16.5 Results and Discussion
	16.6 Conclusion and Future Scope
	References
Chapter 17 Artificial Intelligence Enables Agile Software Development Life Cycle
	17.1 Introduction
	17.2 Literature Survey
	17.3 Proposed Work
		17.3.1 Advantages and Limitations of Agile Software Development
	17.4 Conclusion
	References
Chapter 18 Machine Learning in ASD: An Intensive Study of Automated Disease Prediction System
	18.1 Introduction
	18.2 Overview of ML
		18.2.1 Types of Machine Learning
			18.2.1.1 Supervised Machine Learning
			18.2.1.2 Unsupervised Machine Learning
			18.2.1.3 Reinforcement ML
		18.2.2 Popular ML Algorithm
			18.2.2.1 Artificial Neural Network (ANN)
			18.2.2.2 K-Means Clustering Algorithm
			18.2.2.3 Hierarchical Clustering
			18.2.2.4 Linear Regression in Machine Learning
			18.2.2.5 Support Vector Machine (SVM)
			18.2.2.6 Decision Tree
			18.2.2.7 Random Forests
			18.2.2.8 Agile Software Development (ASD)
	18.3 Case Study
		18.3.1 Methodology
		18.3.2 Result Analysis
	18.4 Conclusion
	References
Index
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