Optimising The Software Development Process With Artificial Intelligence
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Optimising The Software Development Process With Artificial Intelligence
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Author : José Raúl Romero
language : en
Publisher: Springer Nature
Release Date : 2023-07-19
Optimising The Software Development Process With Artificial Intelligence written by José Raúl Romero and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-19 with Computers categories.
This book offers a practical introduction to the use of artificial intelligence (AI) techniques to improve and optimise the various phases of the software development process, from the initial project planning to the latest deployment. All chapters were written by leading experts in the field and include practical and reproducible examples. Following the introductory chapter, Chapters 2-9 respectively apply AI techniques to the classic phases of the software development process: project management, requirement engineering, analysis and design, coding, cloud deployment, unit and system testing, and maintenance. Subsequently, Chapters 10 and 11 provide foundational tutorials on the AI techniques used in the preceding chapters: metaheuristics and machine learning. Given its scope and focus, the book represents a valuable resource for researchers, practitioners and students with a basic grasp of software engineering.
Artificial Intelligence Methods For Optimization Of The Software Testing Process
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Author : Sahar Tahvili
language : en
Publisher: Elsevier
Release Date : 2022-07-26
Artificial Intelligence Methods For Optimization Of The Software Testing Process written by Sahar Tahvili and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-26 with Computers categories.
Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way. As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys. To learn more about Elsevier's Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence Presents one of the first empirical studies in the field, contrasting theoretical assumptions on innovations in a real industrial environment with a large set of use cases from developed and developing testing processes at various large industries Explores specific comparative methodologies, focusing on developed and developing AI-based solutions Serves as a guideline for conducting industrial research in the artificial intelligence and software testing domain Explains all proposed solutions through real industrial case studies
Artificial Intelligence Methods For Software Engineering
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Author : Meir Kalech
language : en
Publisher: World Scientific
Release Date : 2021-06-15
Artificial Intelligence Methods For Software Engineering written by Meir Kalech and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-15 with Computers categories.
Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s)
Artificial Intelligence Methods For Optimization Of The Software Testing Process
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Author : Sahar Tahvili
language : en
Publisher: Academic Press
Release Date : 2022-07-21
Artificial Intelligence Methods For Optimization Of The Software Testing Process written by Sahar Tahvili and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-21 with Computers categories.
Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way. As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys. To learn more about Elsevier's Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence - Presents one of the first empirical studies in the field, contrasting theoretical assumptions on innovations in a real industrial environment with a large set of use cases from developed and developing testing processes at various large industries - Explores specific comparative methodologies, focusing on developed and developing AI-based solutions - Serves as a guideline for conducting industrial research in the artificial intelligence and software testing domain - Explains all proposed solutions through real industrial case studies
Handbook Of Intelligent Computing And Optimization For Sustainable Development
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Author : Mukhdeep Singh Manshahia
language : en
Publisher: John Wiley & Sons
Release Date : 2022-02-11
Handbook Of Intelligent Computing And Optimization For Sustainable Development written by Mukhdeep Singh Manshahia and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-11 with Technology & Engineering categories.
HANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries. Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local conditions. The 41 chapters comprising the Handbook of Intelligent Computing and Optimization for Sustainable Development by subject specialists, represent diverse disciplines such as mathematics and computer science, electrical and electronics engineering, neuroscience and cognitive sciences, medicine, and social sciences, and provide the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including IoT, manufacturing, optimization, and healthcare. Audience It is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in the field of artificial intelligence in the areas of Internet of Things, renewable energy, optimization, and smart cities.
Machine Learning For Software Quality And Reliability Transforming Software Engineering
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Author : Hariprasad Sivaraman
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2020-11-13
Machine Learning For Software Quality And Reliability Transforming Software Engineering written by Hariprasad Sivaraman and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-13 with Computers categories.
As software systems grow in complexity, ensuring quality, reliability, and efficiency has become more challenging than ever. Machine Learning for Software Quality and Reliability explores how AI-driven techniques are revolutionizing software engineering, enhancing defect prediction, automating testing, and optimizing performance. This book provides a deep dive into machine learning applications across the software development lifecycle. Readers will discover how ML models improve defect detection, automate test case generation, and enhance predictive maintenance, reducing downtime and increasing system stability. Key topics include supervised and unsupervised learning for software quality, anomaly detection for reliability engineering, and ML- powered root cause analysis. By integrating real-world case studies from leading technology companies, the book demonstrates how AI- driven quality assurance improves efficiency and scalability across industries. Learn how Google, Microsoft, and Netflix leverage ML to enhance software reliability, minimize bugs, and optimize continuous integration/continuous deployment (CI/CD) pipelines. With a focus on practical implementation, Machine Learning for Software Quality and Reliability equips software engineers, IT leaders, and quality assurance professionals with actionable insights to integrate ML into their workflows. Whether you ' re seeking to automate testing, enhance system resilience, or improve software reliability, this book provides the roadmap to harnessing AI for the next generation of high-quality software development.
Integrating Artificial Intelligence With Devops
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Author : Sumanth Tatineni
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2024-03-15
Integrating Artificial Intelligence With Devops written by Sumanth Tatineni and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-15 with Computers categories.
Unlock the future of software development with Integrating Artificial Intelligence with DevOps: Advanced Techniques, Predictive Analytics, and Automation for Real-Time Optimization and Security in Modern Software Development. This comprehensive monograph is a must-read for professionals seeking to revolutionize their DevOps workflows through the power of AI. Dive deep into the intricate integration of Artificial Intelligence within DevOps practices and discover advanced methodologies that enhance every stage of the software development lifecycle. From predictive analytics and intelligent automation to real-time optimization and robust security measures, this book offers a wealth of knowledge for optimizing software delivery. Explore practical applications, in-depth case studies, and best practices that illustrate the transformative potential of AI in DevOps. Each chapter builds on the previous, providing a seamless and cohesive narrative that guides readers through foundational concepts to advanced implementations. Whether you're looking to improve CI/CD pipelines, automate testing and monitoring, manage infrastructure more efficiently, or enhance security with AI-driven techniques, this book equips you with the tools and insights needed to ensure high-quality, secure, and efficient software delivery. Join the vanguard of modern software development with Integrating Artificial Intelligence with DevOps, and harness AI to achieve real-time optimization and unparalleled security in your DevOps processes.
Machine Learning Optimization And Data Science
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Author : Giuseppe Nicosia
language : en
Publisher: Springer Nature
Release Date : 2025-03-03
Machine Learning Optimization And Data Science written by Giuseppe Nicosia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-03 with Computers categories.
The three-volume set LNAI 15508-15510 constitutes the refereed proceedings of the 10th International Conference on Machine Learning, Optimization, and Data Science, LOD 2024, held in Castiglione della Pescaia, Italy, during September 22–25, 2024. This year, in the LOD Proceedings decided to also include the papers of the fourth edition of the Symposium on Artificial Intelligence and Neuroscience (ACAIN 2024). The 79 full papers included in this book were carefully reviewed and selected from 127 submissions. The LOD 2024 proceedings focus on machine learning, deep learning, AI, computational optimization, neuroscience and big data that includes invited talks, tutorial talks, special sessions, industrial tracks, demonstrations and oral and poster presentations of refereed papers.
Artificial Intelligence For Business Optimization
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Author : Bhuvan Unhelkar
language : en
Publisher: CRC Press
Release Date : 2021-08-09
Artificial Intelligence For Business Optimization written by Bhuvan Unhelkar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-09 with Computers categories.
This book explains how AI and Machine Learning can be applied to help businesses solve problems, support critical thinking and ultimately create customer value and increase profit. By considering business strategies, business process modeling, quality assurance, cybersecurity, governance and big data and focusing on functions, processes, and people’s behaviors it helps businesses take a truly holistic approach to business optimization. It contains practical examples that make it easy to understand the concepts and apply them. It is written for practitioners (consultants, senior executives, decision-makers) dealing with real-life business problems on a daily basis, who are keen to develop systematic strategies for the application of AI/ML/BD technologies to business automation and optimization, as well as researchers who want to explore the industrial applications of AI and higher-level students.
Data Democracy
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Author : Feras A. Batarseh
language : en
Publisher: Academic Press
Release Date : 2020-01-21
Data Democracy written by Feras A. Batarseh and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-21 with Science categories.
Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering provides a manifesto to data democracy. After reading the chapters of this book, you are informed and suitably warned! You are already part of the data republic, and you (and all of us) need to ensure that our data fall in the right hands. Everything you click, buy, swipe, try, sell, drive, or fly is a data point. But who owns the data? At this point, not you! You do not even have access to most of it. The next best empire of our planet is one who owns and controls the world's best dataset. If you consume or create data, if you are a citizen of the data republic (willingly or grudgingly), and if you are interested in making a decision or finding the truth through data-driven analysis, this book is for you. A group of experts, academics, data science researchers, and industry practitioners gathered to write this manifesto about data democracy. - The future of the data republic, life within a data democracy, and our digital freedoms - An in-depth analysis of open science, open data, open source software, and their future challenges - A comprehensive review of data democracy's implications within domains such as: healthcare, space exploration, earth sciences, business, and psychology - The democratization of Artificial Intelligence (AI), and data issues such as: Bias, imbalance, context, and knowledge extraction - A systematic review of AI methods applied to software engineering problems