Machine Learning For Software Quality And Reliability Transforming Software Engineering
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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.
Machine Learning Applications In Software Engineering
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Author : Du Zhang
language : en
Publisher: World Scientific
Release Date : 2005
Machine Learning Applications In Software Engineering written by Du Zhang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
A collection of previously published articles from a variety of publications.
Analytics Modeling In Reliability And Machine Learning And Its Applications
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Author : Hoang Pham
language : en
Publisher: Springer Nature
Release Date : 2025-01-20
Analytics Modeling In Reliability And Machine Learning And Its Applications written by Hoang Pham and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-20 with Mathematics categories.
This book presents novel research and application chapters on topics in reliability, statistics, and machine learning. It has an emphasis on analytical models and techniques and practical applications in reliability engineering, data science, manufacturing, health care, and industry using machine learning, AI, optimization, and other computational methods. Today, billions of people are connected to each other through their mobile devices. Data is being collected and analysed more than ever before. The era of big data through machine learning algorithms, statistical inference, and reliability computing in almost all applications has resulted in a dramatic shift in the past two decades. Data analytics in business, finance, and industry is vital. It helps organizations and business to achieve better results and fact-based decision-making in all aspects of life. The book offers a broad picture of current research on the analytics modeling and techniques and its applications in industry. Topics include: l Reliability modeling and methods. l Software reliability engineering. l Maintenance modeling and policies. l Statistical feature selection. l Big data modeling. l Machine learning: models and algorithms. l Data-driven models and decision-making methods. l Applications and case studies in business, health care, and industrial systems. Postgraduates, researchers, professors, scientists, engineers, and practitioners in reliability engineering and management, machine learning engineering, data science, operations research, industrial and systems engineering, statistics, computer science and engineering, mechanical engineering, and business analytics will find in this book state-of-the-art analytics, modeling and methods in reliability and machine learning.
Generative Ai In Software Engineering
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Author : Aguilar-Calderón, José Alfonso
language : en
Publisher: IGI Global
Release Date : 2025-06-13
Generative Ai In Software Engineering written by Aguilar-Calderón, José Alfonso and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-13 with Computers categories.
Generative AI transforms the landscape of software engineering, enabling automation, creativity, and efficiency throughout development. By leveraging advanced machine learning models, like large language models and code generation tools, developers can automate code generation, streamline testing, and design software architectures. This shift accelerates development timelines and redefines the roles of engineers and the skills required in modern software teams. As generative AI evolves, its integration into software engineering raises important questions around reliability, security, and human-AI collaboration. Generative AI in Software Engineering explores the evolving role of generative AI in the software engineering landscape. It examines how AI accelerates software development, reduces costs, and enhances creativity, offering real-world benefits for businesses. This book covers topics such as quantum computing, visual intelligence, and environment science, and is a useful resource for business owners, computer engineers, academicians, researchers, and data scientists.
Digitalization Strategy For Business Transformation
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Author : Dr. Ahmad Al Naqeeb
language : en
Publisher: IPR Journals and Book Publishers
Release Date : 2023-09-28
Digitalization Strategy For Business Transformation written by Dr. Ahmad Al Naqeeb and has been published by IPR Journals and Book Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-28 with Business & Economics categories.
TOPICS IN THE BOOK Leading Technological Change – Case Study of Emirates Airlines Developing Conceptual Framework of Software Defect Prediction in Software Testing: The Case of Ethiopian Software Industries Assessment of Level of Service for Roads under Performance Based Road Maintenance in Kenya Precursors of Cloud Computing Adoption in Selected Banks in Kenya Factors Affecting Adoption of Internet of Things in Selected Greenhouse Farms in Kenya
Continuous Testing Quality Security And Feedback
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Author : Marc Hornbeek
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-05
Continuous Testing Quality Security And Feedback written by Marc Hornbeek and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-05 with Computers categories.
A step-by-step guide to developing high-quality, secure, and agile software using continuous testing and feedback strategies and tools Key Features Gain insights from real-world use cases and experiences of an IEEE Outstanding Engineer and DevOps consultant Implement best practices for continuous testing strategies and tools, test designs, environments, results, and metrics Leverage AI/ML, implementation patterns, and performance measurement during software development Book DescriptionOrganizations struggle to integrate and execute continuous testing, quality, security, and feedback practices into their DevOps, DevSecOps, and SRE approaches to achieve successful digital transformations. This book addresses these challenges by embedding these critical practices into your software development lifecycle. Beginning with the foundational concepts, the book progresses to practical applications, helping you understand why these practices are crucial in today’s fast-paced software development landscape. You’ll discover continuous strategies to avoid the common pitfalls and streamline the quality, security, and feedback mechanisms within software development processes. You’ll explore planning, discovery, and benchmarking through systematic engineering approaches, tailored to organizational needs. You’ll learn how to select toolchains, integrating AI/ML for resilience, and implement real-world case studies to achieve operational excellence. You’ll learn how to create strategic roadmaps, aligned with digital transformation goals, and measure outcomes recognized by DORA. You’ll explore emerging trends that are reshaping continuous practices in software development. By the end of this book, you’ll have the knowledge and skills to drive continuous improvement across the software development lifecycle.What you will learn Ensure continuous testing, quality, security, and feedback in DevOps, DevSecOps, and SRE practices Apply capability maturity models, set goals, conduct discoveries, and set benchmarks for digital transformations Implement and assess continuous improvement strategies with various tools and frameworks Avoid pitfalls and enhance user experience with gap assessments, value stream management, and roadmaps Adhere to proven engineering practices for software delivery and operations Stay on top of emerging trends in AI/ML and continuous improvement Who this book is for This book is for software engineers, DevOps engineers, DevSecOps engineers, site reliability engineers, testers, QA professionals, and enterprise leaders looking to implement continuous testing, quality, security, and feedback for achieving efficiency, reliability, and success in digital transformations. Basic knowledge and experience in software development, testing, system design and system operations is a must.
Meta Heuristic Techniques In Software Engineering And Its Applications
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Author : Mihir Narayan Mohanty
language : en
Publisher: Springer Nature
Release Date : 2022-10-17
Meta Heuristic Techniques In Software Engineering And Its Applications written by Mihir Narayan Mohanty and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-17 with Technology & Engineering categories.
This book discusses an integration of machine learning with metaheuristic techniques that provide more robust and efficient ways to address traditional optimization problems. Modern metaheuristic techniques, along with their main characteristics and recent applications in artificial intelligence, software engineering, data mining, planning and scheduling, logistics and supply chains, are discussed in this book and help global leaders in fast decision making by providing quality solutions to important problems in business, engineering, economics and science. Novel ways are also discovered to attack unsolved problems in software testing and machine learning. The discussion on foundations of optimization and algorithms leads beginners to apply current approaches to optimization problems. The discussed metaheuristic algorithms include genetic algorithms, simulated annealing, ant algorithms, bee algorithms and particle swarm optimization. New developments on metaheuristics attract researchers and practitioners to apply hybrid metaheuristics in real scenarios.
Advancing Software Engineering Through Ai Federated Learning And Large Language Models
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Author : Sharma, Avinash Kumar
language : en
Publisher: IGI Global
Release Date : 2024-05-02
Advancing Software Engineering Through Ai Federated Learning And Large Language Models written by Sharma, Avinash Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-02 with Computers categories.
The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics.
Industrial Applications Of Machine Learning
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Author : Pedro Larrañaga
language : en
Publisher: CRC Press
Release Date : 2018-12-12
Industrial Applications Of Machine Learning written by Pedro Larrañaga and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-12 with Business & Economics categories.
Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka
Boosting Software Development Using Machine Learning
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Author : Tirimula Rao Benala
language : en
Publisher: Springer Nature
Release Date : 2025-05-23
Boosting Software Development Using Machine Learning written by Tirimula Rao Benala and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Computers categories.
This book explores the transformative effects of AI and ML on software engineering. It emphasizes the potential of cutting-edge software development technologies such as Generative AI and ML applications. This book incorporates data-driven strategies across the entire software development life cycle, from requirements elicitation and design to coding, testing, and deployment. It illustrates the evolution from traditional frameworks to agile and DevOps methodologies. The potential of Generative AI for automating repetitive tasks and enhancing code quality is highlighted, along with ML applications in optimizing testing, effort estimation, design pattern recognition, fault prediction, debugging, and security through anomaly detection. These techniques have significantly improved software development efficiency, predictability, and project management effectiveness. While remarkable progress has been made, much remains to be done in this evolving area. This edited book is a timely effort toward advancing the field and promoting interdisciplinary collaboration in addressing ethical, security, and technical challenges.