System Assurances

System Assurances

Modeling and Management

1st Edition - February 16, 2022

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  • Editors: Prashant Johri, Adarsh Anand, Juri Vain, Jagvinder Singh, Mohammad Quasim
  • Paperback ISBN: 9780323902403
  • eBook ISBN: 9780323902410

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Description

System Assurances: Modeling and Management updates on system assurance and performance methods using advanced analytics and understanding of software reliability growth modeling from today’s debugging team’s point-of-view, along with information on preventive and predictive maintenance and the efficient use of testing resources. The book presents the rapidly growing application areas of systems and software modeling, including intelligent synthetic characters, human-machine interface, menu generators, user acceptance analysis, picture archiving and software systems. Students, research scholars, academicians, scientists and industry practitioners will benefit from the book as it provides better insights into modern related global trends, issues and practices.

Key Features

  • Provides software reliability modeling, simulation and optimization
  • Offers methodologies, tools and practical applications of reliability modeling and resources allocation
  • Presents cost modeling and optimization associated with complex systems

Readership

Academia, Researcher in software engineering and information sciences working in the field of modelling. Undergraduate and Postgraduate in engineering. Professionals in research groups of large companies, Universities and Research institutes involved in the epidemiology analysis and working to fight against global outbreaks

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • Preface
  • Acknowledgments
  • Chapter 1: Statistical analysis approach for the quality assessment of open-source software
  • Abstract
  • Acknowledgment
  • 1.1: Introduction
  • 1.2: Correspondence analysis
  • 1.3: Estimation procedure based on correspondence analysis
  • 1.4: Numerical examples
  • 1.5: Concluding remarks
  • References
  • Chapter 2: Analytical modeling and performance evaluation of SIP signaling protocol: Analytical modeling of SIP
  • Abstract
  • Acknowledgments
  • 2.1: Introduction
  • 2.2: Background work
  • 2.3: SIP layered structure and its working
  • 2.4: Proposed SRN model of SIP INVITE transaction
  • 2.5: Performance measures
  • 2.6: Numerical illustration
  • 2.7: Model validation
  • 2.8: Conclusions
  • References
  • Chapter 3: An empirical validation for predicting bugs and the release time of open source software using entropy measures—Software reliability growth models
  • Abstract
  • 3.1: Introduction
  • 3.2: Information measures
  • 3.3: Conclusion
  • References
  • Chapter 4: Risk assessment of starting air system of marine diesel engine using fuzzy failure mode and effects analysis
  • Abstract
  • 4.1: Introduction
  • 4.2: Starting air system
  • 4.3: Failure mode and effects analysis (FMEA)
  • 4.4: The proposed methodology
  • 4.5: An illustrative example: Starting air system
  • 4.6: Results
  • 4.7: Sensitivity analysis
  • 4.8: Conclusions
  • References
  • Chapter 5: Test scenario generator learning for model-based testing of mobile robots
  • Abstract
  • 5.1: Introduction
  • 5.2: Related work
  • 5.3: Preliminaries
  • 5.4: Tool architecture
  • 5.5: Validation
  • 5.6: Conclusion
  • References
  • Chapter 6: Testing effort-dependent software reliability growth model using time lag functions under distributed environment
  • Abstract
  • 6.1: Introduction
  • 6.2: Software reliability growth modeling
  • 6.3: Parameter estimation
  • 6.4: Comparison of SRGM criteria
  • 6.5: Data description and model validation
  • 6.6: Conclusion
  • References
  • Chapter 7: Design and performance analysis of MIMO PID controllers for a paper machine subsystem
  • Abstract
  • 7.1: Introduction
  • 7.2: Controller tuning
  • 7.3: Result analysis
  • 7.4: Conclusion
  • References
  • Chapter 8: Network and security leveraging IoT and image processing: A quantum leap forward
  • Abstract
  • 8.1: Introduction
  • 8.2: Comparative study
  • 8.3: Conclusion
  • References
  • Chapter 9: Modeling software patching process inculcating the impact of vulnerabilities discovered and disclosed
  • Abstract
  • Acknowledgment
  • 9.1: Introduction
  • 9.2: Literature review
  • 9.3: Notations
  • 9.4: Model development
  • 9.5: Model illustration
  • 9.6: Conclusion
  • References
  • Chapter 10: Extension of software reliability growth models by several testing-time functions
  • Abstract
  • Acknowledgments
  • 10.1: Introduction
  • 10.2: Software reliability assessment using bivariate SRGMs
  • 10.3: Bivariate Weibull-type SRGMs and their application
  • 10.4: Conclusions
  • References
  • Chapter 11: A semi-Markov model of a system working under uncertainty
  • Abstract
  • Acknowledgment
  • 11.1: Introduction
  • 11.2: Notations
  • 11.3: Development of system model
  • 11.4: Performance measures
  • 11.5: Simulation study
  • 11.6: Concluding remarks
  • References
  • Chapter 12: Design and evaluation of parallel-series IRM system
  • Abstract
  • 12.1: Introduction
  • 12.2: Lagrangean procedure for formulation of the problem function: cj=bjeaj1−rj
  • 12.3: Case problem
  • 12.4: Dynamic programming approach
  • 12.5: The integrated efficiency model by using the dynamic programming method for the function cj=bjeaj1−rj
  • 12.6: Conclusions
  • Further reading
  • Chapter 13: Modeling and availability assessment of smart building automation systems with multigoal maintenance
  • Abstract
  • 13.1: Introduction
  • 13.2: Concept of multigoal maintenance
  • 13.3: Development of models
  • 13.4: Research of models
  • 13.5: Conclusions
  • References
  • Chapter 14: A study of bitcoin and Ethereum blockchains in the context of client types, transactions, and underlying network architecture
  • Abstract
  • 14.1: Blockchain: An insight
  • 14.2: Blockchain architectures
  • 14.3: Blockchain adequacy
  • 14.4: Bitcoin architecture
  • 14.5: Introduction of Ethereum
  • 14.6: Ethereum’s evolution
  • 14.7: Architecture of Ethereum
  • 14.8: Ethereum components
  • 14.9: Conclusion
  • References
  • Further reading
  • Chapter 15: High assurance software architecture and design
  • Abstract
  • 15.1: Introduction
  • 15.2: Software architecture patterns
  • 15.3: Software design principles
  • 15.4: Software design patterns
  • 15.5: Software design antipatterns
  • 15.6: Conclusion
  • References
  • Chapter 16: Online condition monitoring and maintenance of photovoltaic system
  • Abstract
  • 16.1: Introduction
  • 16.2: Condition monitoring of VRLA battery in PV system
  • 16.3: Condition monitoring of aluminum electrolytic capacitor and MOSFET of power converter in PV system
  • 16.4: Conclusions
  • References
  • Chapter 17: Fault diagnosis and fault tolerance
  • Abstract
  • Acknowledgment
  • 17.1: Introduction
  • 17.2: Digital systems modeling
  • 17.3: Fault models
  • 17.4: Fault diagnosis test procedures
  • 17.5: Fault diagnosis and fault tolerance
  • 17.6: Conclusions
  • References
  • Chapter 18: True power loss diminution by Improved Grasshopper Optimization Algorithm
  • Abstract
  • 18.1: Introduction
  • 18.2: Problem formulation
  • 18.3: Improved Grasshopper Optimization Algorithm
  • 18.4: Simulation study
  • 18.5: Conclusions
  • References
  • Chapter 19: Security analytics
  • Abstract
  • 19.1: Introduction
  • 19.2: Different classes of security analytics
  • 19.3: Framework of cyber security analytics
  • 19.4: Big data security analytics
  • 19.5: Security analytics for IoT
  • 19.6: Security analytics in anomaly detection
  • References
  • Chapter 20: Stochastic modeling of the mean time between software failures: A review
  • Abstract
  • Acknowledgments
  • 20.1: Introduction
  • 20.2: Mathematical background
  • 20.3: Application
  • 20.4: Conclusions
  • References
  • Chapter 21: Inliers prone distributions: Perspectives and future scopes
  • Abstract
  • 21.1: Introduction
  • 21.2: Inliers prone models
  • 21.3: Inferences on 0–1 inliers model
  • 21.4: Tests of hypothesis about inliers
  • 21.5: Data analysis
  • 21.6: Inliers-prone distributions: Issues and problems
  • 21.7: Future scopes
  • References
  • Chapter 22: Integration of TPM, RCM, and CBM: A practical approach applied in Shipbuilding industry
  • Abstract
  • 22.1: Introduction
  • 22.2: Maintenance strategies
  • 22.3: Reliability of marine propulsion system: A case study
  • 22.4: Conclusion
  • References
  • Chapter 23: Revolutionizing the internet of things with swarm intelligence
  • Abstract
  • 23.1: Introduction
  • 23.2: Characteristics of IoT
  • 23.3: The consumer IoT
  • 23.4: The industrial IoT
  • 23.5: IoT definitions by various companies
  • 23.6: The industrial internet
  • 23.7: The internet of everything (IoE)
  • 23.8: Cyber physical systems (CPS) and industry 4.0
  • 23.9: The internet of services (IoS)
  • 23.10: The internet of robotic things (IoRT)
  • 23.11: More internet of X terms
  • 23.12: Swarm intelligence
  • 23.13: Definitions of SI
  • 23.14: SI and systems intersections
  • 23.15: Swarm Intelligence and smart gadgets
  • 23.16: Implants & prosthetics
  • 23.17: The swarm behavior of the augmented and quantified human—The next stage of the IoE?
  • 23.18: ANT colony-based IoT systems
  • 23.19: ANT colony optimization (ACO)-based IoT systems
  • 23.20: Particle swarm optimization (PSO)-based IoT systems
  • 23.21: Artificial bee colony (ABC)-based IoT systems
  • 23.22: Bacterial foraging optimization (BFO)-based IoT systems
  • 23.23: BAT optimization (BO)-based IoT systems
  • 23.24: More SI-based IoT systems
  • 23.25: Towards SI-based IoT systems
  • 23.26: SI and its applications
  • 23.27: SI application for IoT processes
  • 23.28: Conclusion
  • References
  • Chapter 24: Security and challenges in IoT-enabled systems
  • Abstract
  • 24.1: Introduction
  • 24.2: Commercialized secure hardware primitive designs
  • 24.3: Hardware trojan
  • 24.4: Side-channel attack (SCA)
  • 24.5: Reverse engineering
  • 24.6: Key challenges
  • 24.7: Conclusion
  • References
  • Chapter 25: Provably correct aspect-oriented modeling with UPPAAL timed automata
  • Abstract
  • Acknowledgment
  • 25.1: Introduction
  • 25.2: Related work
  • 25.3: Preliminaries
  • 25.4: Provably correct weaving of aspects
  • 25.5: Case study: Home rehabilitation system
  • 25.6: Usability of AO modeling and verification
  • 25.7: Conclusions and discussion
  • References
  • Chapter 26: Relevance of data mining techniques in real life
  • Abstract
  • 26.1: Introduction
  • 26.2: Methodology
  • 26.3: Need of data mining
  • 26.4: Types of data mining
  • 26.5: Data mining techniques
  • 26.6: Categories of data mining techniques
  • 26.7: Applications of data mining methods
  • 26.8: Conclusion
  • References
  • Chapter 27: D-PPSOK clustering algorithm with data sampling for clustering big data analysis
  • Abstract
  • 27.1: Introduction
  • 27.2: Related work
  • 27.3: Proposed work
  • 27.4: Experimental result and discussion
  • 27.5: Conclusion
  • References
  • Chapter 28: A review on optimal placement of phasor measurement unit (PMU)
  • Abstract
  • 28.1: Introduction
  • 28.2: Optimal PMU placement (OPP) problem formulation
  • 28.3: Mathematical programming method
  • 28.4: Meta-heuristic methods
  • 28.5: Heuristic methods
  • 28.6: Algorithm comparison
  • 28.7: Future scope
  • 28.8: Conclusion
  • References
  • Chapter 29: Effective motivational factors and comprehensive study of information security and policy challenges
  • Abstract
  • 29.1: Introduction
  • 29.2: Key information security policies-related challenges
  • 29.3: Organizational approaches to information security
  • 29.4: Network architecture and threat model
  • 29.5: Policy-based SDN security architecture
  • 29.6: Trust
  • 29.7: Privacy
  • 29.8: Privacy and security in cloud computing
  • 29.9: Cloud computing framework
  • 29.10: ABE in cloud computing
  • 29.11: Conclusions
  • References
  • Chapter 30: Integration of wireless communication technologies in internet of vehicles for handover decision and network selection
  • Abstract
  • 30.1: Introduction
  • 30.2: Existing works
  • 30.3: Research method
  • 30.4: Simulation setup
  • 30.5: Comparative analysis and results
  • 30.6: Conclusion
  • References
  • Chapter 31: Modeling HIV-TB coinfection with illegal immigrants and its stability analysis
  • Abstract
  • Acknowledgments
  • 31.1: Introduction
  • 31.2: The mathematical model
  • 31.3: The mathematical analysis
  • 31.4: Numerical analysis and discussion
  • References
  • Further reading
  • Index

Product details

  • No. of pages: 614
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: February 16, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323902403
  • eBook ISBN: 9780323902410

About the Editors

Prashant Johri

Dr. Prashant Johri. Professor in School of Computing Science & Engineering, Galgotias University, Greater Noida, India. He completed his B.Sc.(H) in 1992 and M.C.A. in 1992 from A.M.U, Aligarh and Ph.D. in Computer Science from Jiwaji University, Gwalior in 2011, India. He has also worked as a Professor and Director (M.C.A.), Galgotias Institute of Management and Technology and Noida Institute of Engineering and Technology, Gr. Noida. He has served as Chair in many conferences in India and Abroad. He has supervised 2 PhD students and M. Tech. students. He published more than 100 research papers in National and International Journals and Conferences. He has published edited books in Elsevier and Springer. He organized several Conferences / Workshops/Seminars at the national and international levels. His research interest includes Artificial Intelligence, Machine Learning, Data Science, Deep Reinforcement Learning, Information Security, Cloud Computing, Block Chain, Healthcare, Agriculture, Image Processing, Software Reliability.

Affiliations and Expertise

Professor, School of Computing Science and Engineering, Galgotias University, Greater Noida, India

Adarsh Anand

Dr. Adarsh Anand did his Ph.D. in the area of Operational Research. Presently he is working as an Assistant Professor in the Department of Operational Research, University of Delhi (INDIA). He has been conferred with Young Promising Researcher in the field of Technology Management and Software Reliability by Society for Reliability Engineering, Quality and Operations Management in 2012. He is a lifetime member of the Society for Reliability Engineering, Quality and Operations Management. He is also on the editorial board of International Journal of Mathematical, Engineering and Management Sciences. He has edited two books entitled “System reliability Management: Solutions & Technologies” and “Recent Advancements in Software Reliability Assurance”; both by CRC Press Taylor & Francis Group. He has Guest edited several Special Issues for Journals of international repute. His research interest includes software reliability growth modelling, modelling innovation adoption and successive generations in marketing, and social network analysis.

Affiliations and Expertise

Assistant Professor, Departmetn of Operational Research, University of Delhi, New Delhi, India

Juri Vain

Prof. Juri Vain graduated in System Engineering from Tallinn Polytechnic Institute, Estonia in 1979. He received his PhD in computer science from the Institute of Cybernetics at Estonian Academy of Sciences in 1987. Currently, he is Tenure Professor of Formal Methods at the Department of Software Science, Tallinn University of Technology. His research interests include formal methods, model-based testing, cyber physical systems, human-computer interaction, autonomous robotics, and artificial intelligence. He has been leading researcher in several international projects under EU framework programs and Centre of Excellence on Human Adaptive Mechatronics at Tokyo Denki University. He has published 200 scientific articles including journal papers, book chapters and conference papers. He has been invited speaker at many international conferences and summer schools. Under his supervision 8 PhD thesis and more than 20 MSc thesis have been defended. He is teaching formal methods, model-based testing and constraint logic programming

Affiliations and Expertise

Tenure Professor of Formal Methods, Department of Software Science, Tallinn University of Technology, Tallinn, Estonia

Jagvinder Singh

Dr. Jagvinder Singh is an Assistant Professor at USME, DTU East Campus. He has completed his Ph.D (Operations Research) as well as M.sc (Operations Research) from University of Delhi (D.U). He has more than 8 years of teaching experience at University of Delhi in teaching the students of graduation as well as post graduation level. He has more than 30 research papers published to his credit at various reputed national as well as international journals some of them are published by renowned publishing houses such as Elsevier, Emerald, Taylor &Francis, Springer etc. His area of specialization is Mathematical Modelling in software reliability and management sciences. He is also involved as a supervisor and is guiding 4 of his research scholars.

Affiliations and Expertise

Assistant Professor, USME, DTU East Campus, India

Mohammad Quasim

Dr.Mohammd Tabrez Quasim received his Ph.D ( Computer Science) from Tilkamanjhi Bhagalpur University and M.C.A. from Punjab Technical University, Punjab, India. Presently he is working as Assistant Professor at University of Bisha, Saudi Arabia. His research interests include but are not limited to IOT, Big Data, Cloud Computing, Blockchain, Wireless Sensors Networks. He has more than 10 year of experience in his research area. He has published many journal articles, Edited Book, Book Chapters and conference papers in various internationally recognized academic databases. He is contributing to the research community by various volunteer activities in the capacity of editor for many journal and conference chair in various reputed IEEE/Springer.

Affiliations and Expertise

Assistant Professor, Univeristy of Bisha, Saudi Arabia

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