Innovative Learning Methodologies For Enhancing Software Quality In Devops
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Innovative Learning Methodologies For Enhancing Software Quality In Devops
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Author : Chaitra K.M
language : en
Publisher:
Release Date : 2020
Innovative Learning Methodologies For Enhancing Software Quality In Devops written by Chaitra K.M and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
DevOps is evolved from agile methodology that integrates the operation and development teams to enhance the relationship and strategy between them. The motive is to improve the software quality, security and business strategies. According to literature review, the researcher improved software quality by testing techniques like Pearson correlation, linear regression analysis and CAMS (Culture, Automation, Measurement, Sharing) framework. We adopted BizDevOps for continuous innovation in business and DevSecOps for security purpose. ISO 9126-1 is the latest proposal used for software quality management. The main implication of this paper is to underline the security, business and quality strategies to achieve good quality software and deliver services rapidly.
Ai Powered Software Quality Assurance Transforming Testing With Ai And Machine Learning
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Author : Amit Bhanushali
language : en
Publisher: Xoffencerpublication
Release Date : 2023-10-30
Ai Powered Software Quality Assurance Transforming Testing With Ai And Machine Learning written by Amit Bhanushali and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-30 with Computers categories.
New challenges have arisen for the construction of contemporary AI-based systems as a result of recent developments in artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), and their incorporation into software-based systems utilized in all sectors of the economy. These systems place a heavy reliance on data, are constantly evolving and bettering themselves, and display a degree of intrinsic nondeterminism. As a consequence, their behavior displays a degree of uncertainty that is universally acknowledged. As a result of these characteristics, the field of software engineering has to devise adaptable and innovative approaches to quality assurance (QA) that are capable of both constructive and in-depth analysis. This is essential in order to guarantee the product's high quality throughout the whole development process as well as while it is being put to use in actual settings. On the other hand, as Borg has pointed out, the concept of "quality" in AI-based systems does not yet have a definitive definition at this time. As was noted before, the terminology that is utilized in the field of artificial intelligence and software engineering is distinct from one another. When developing AI-based systems, the knowledge and experiences of a wide variety of organizations are combined and utilized in the construction process. While this does lead to new and creative ways, exciting breakthroughs, and a major advancement in what can be done with current AI-based systems, it also encourages the babel of language, concepts, perceptions, and underlying assumptions and principles. While this does lead to new and creative methods, exciting breakthroughs, and a substantial advancement in what can be done with current AI-based systems, this does lead to new and creative approaches. While this does result in novel and creative methods, exciting discoveries, and a significant leap forward in terms of what can be accomplished with contemporary AI-based systems, it does so in spite of the fact that. For instance, in the field of machine learning (ML), the term "regression" may be used to refer to regression models or regression analysis, but in the field of software engineering (SE), the term "regression" is used to refer to regression testing. However, in the context of machine learning (ML), the term "testing" refers to the evaluation of performance characteristics (such as accuracy) of a trained model using a holdout validation dataset. In the context of software engineering (SE), "testing" is described as the activity of executing the system in order to uncover errors. As a consequence of this, there is an increasing amount of confusion, as well as the potential of solutions that are in contradiction with one another, about how to approach quality assurance for AI-based systems and how to deal with the challenges that are associated with it. This is because of the fact that there are a growing number of solutions that are based on AI. Although the authors of this study begin their investigation from the perspective of software engineering, their ultimate goal is to include and talk about a wide variety of different points of view, all of which will eventually come together to provide a multi-dimensional picture of quality assurance for AI-based systems. While the authors of this study begin their investigation from the perspective of software engineering, their ultimate goal is to include and talk about a wide variety of different points of view. In the first part of this study project, our primary focus is on defining the terminologies related with artificial intelligence quality assurance. In the following section, Section 3, we will discuss the challenges that are involved with QA for AI. In the last part of the inquiry, we will summarize what we found and form our conclusions.
Ai Integration In Software Development And Operations
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Author : Abhinav Krishna Kaiser
language : en
Publisher: Springer Nature
Release Date : 2024-12-20
Ai Integration In Software Development And Operations written by Abhinav Krishna Kaiser and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-20 with Computers categories.
Discover how Artificial Intelligence (AI) is transforming the fields of software development, testing, and IT operations by enhancing efficiency, reducing human error, and accelerating processes. This book showcases the practical applications of AI-driven tools, such as automating coding, testing, and operational tasks, predicting potential issues, and optimizing performance. Aimed at digital leaders, practitioners, and customers, this book provides strategic insights and actionable guidance on how to integrate AI technologies to boost productivity, enhance product quality, and streamline development cycles. It serves as a comprehensive guide for those looking to leverage AI to drive innovation, cut costs, and stay competitive in an ever-evolving technological landscape. You’ll explore how AI can be integrated into software development, testing, and IT operations to improve efficiency, accuracy, and speed. Through real-world use cases, you’ll see how AI-driven tools can automate tasks, reduce human error, and improve processes across the development lifecycle. AI Integration in Software Development and Operations offers actionable insights on using AI to accelerate innovation, enhance product quality, and optimize costs in your modern software and IT environments. What You Will Learn Review the SDLC lifecycle, DevOps, SRE and accompanying topics Understand machine learning basics, AI techniques, and data preprocessing for DevOps Explore how AI integration into all phases of SDLC boosts productivity, increases effectiveness, and reduces human error Gain a familiarity with AI tools, their use cases, and the value in integrating them Who This Book is For Software engineers, developers, programmers, DevOps engineers, and AI practitioners who are interested in integrating AI into their DevOps practices.
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.
The Future Of Software Quality Assurance
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Author : Stephan Goericke
language : en
Publisher:
Release Date : 2020-10-09
The Future Of Software Quality Assurance written by Stephan Goericke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-09 with Computers categories.
This open access book, published to mark the 15th anniversary of the International Software Quality Institute (iSQI), is intended to raise the profile of software testers and their profession. It gathers contributions by respected software testing experts in order to highlight the state of the art as well as future challenges and trends. In addition, it covers current and emerging technologies like test automation, DevOps, and artificial intelligence methodologies used for software testing, before taking a look into the future. The contributing authors answer questions like: "How is the profession of tester currently changing? What should testers be prepared for in the years to come, and what skills will the next generation need? What opportunities are available for further training today? What will testing look like in an agile world that is user-centered and fast-paced? What tasks will remain for testers once the most important processes are automated?" iSQI has been focused on the education and certification of software testers for fifteen years now, and in the process has contributed to improving the quality of software in many areas. The papers gathered here clearly reflect the numerous ways in which software quality assurance can play a critical role in various areas. Accordingly, the book will be of interest to both professional software testers and managers working in software testing or software quality assurance. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
The Future Of Software Quality Assurance
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Author : Stephan Goericke
language : en
Publisher: Springer Nature
Release Date : 2019-11-19
The Future Of Software Quality Assurance written by Stephan Goericke and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-19 with Computers categories.
This open access book, published to mark the 15th anniversary of the International Software Quality Institute (iSQI), is intended to raise the profile of software testers and their profession. It gathers contributions by respected software testing experts in order to highlight the state of the art as well as future challenges and trends. In addition, it covers current and emerging technologies like test automation, DevOps, and artificial intelligence methodologies used for software testing, before taking a look into the future. The contributing authors answer questions like: "How is the profession of tester currently changing? What should testers be prepared for in the years to come, and what skills will the next generation need? What opportunities are available for further training today? What will testing look like in an agile world that is user-centered and fast-paced? What tasks will remain for testers once the most important processes are automated?" iSQI has been focused on the education and certification of software testers for fifteen years now, and in the process has contributed to improving the quality of software in many areas. The papers gathered here clearly reflect the numerous ways in which software quality assurance can play a critical role in various areas. Accordingly, the book will be of interest to both professional software testers and managers working in software testing or software quality assurance.
Ai Driven Test Strategies Enhancing Quality And Efficiency In Software Development
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Author : Phani Durga Nanda Kishore Kommisetty
language : en
Publisher: JEC PUBLICATION
Release Date :
Ai Driven Test Strategies Enhancing Quality And Efficiency In Software Development written by Phani Durga Nanda Kishore Kommisetty and has been published by JEC PUBLICATION this book supported file pdf, txt, epub, kindle and other format this book has been release on with Business & Economics categories.
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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.
Shift Left Integrating Test Automation Into Agile Devops Workflows
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Author : Yuvakumar Kuramannagari
language : en
Publisher: International Book Publication House
Release Date : 2025-08-20
Shift Left Integrating Test Automation Into Agile Devops Workflows written by Yuvakumar Kuramannagari and has been published by International Book Publication House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-20 with Business & Economics categories.
Especially within the context of Agile and DevOps processes, the idea of Shift Left has become an essential component of contemporary software development. Organizations are able to uncover errors earlier, decrease the amount of rework that is required, and speed up the delivery of high-quality software when they include test automation at an early stage in the development lifecycle. Shift Left places an emphasis on proactive quality assurance by incorporating automated testing inside continuous integration and continuous delivery (CI/CD) pipelines. This is in contrast to conventional techniques, which place testing at a later stage in the cycle. This integration not only improves the ability of developers, testers, and operations teams to work together, but it also guarantees that quality becomes a responsibility that is shared across the process. The use of Shift Left testing methodologies helps strike a balance between speed and dependability, allowing teams to offer value to consumers more quickly and with more assurance. This is particularly important for enterprises that are becoming increasingly dependent on rapid releases and digital innovation.
Ai Driven Software Testing
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Author : Srinivasa Rao Bittla
language : en
Publisher: Springer Nature
Release Date : 2025-10-21
Ai Driven Software Testing written by Srinivasa Rao Bittla and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-21 with Computers categories.
AI-Driven Software Testing explores how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing quality engineering (QE), making testing more intelligent, efficient, and adaptive. The book begins by examining the critical role of QE in modern software development and the paradigm shift introduced by AI/ML. It traces the evolution of software testing, from manual approaches to AI-powered automation, highlighting key innovations that enhance accuracy, speed, and scalability. Readers will gain a deep understanding of quality engineering in the age of AI, comparing traditional and AI-driven testing methodologies to uncover their advantages and challenges. Moving into practical applications, the book delves into AI-enhanced test planning, execution, and defect management. It explores AI-driven test case development, intelligent test environments, and real-time monitoring techniques that streamline the testing lifecycle. Additionally, it covers AI’s impact on continuous integration and delivery (CI/CD), predictive analytics for failure prevention, and strategies for scaling AI-driven testing across cloud platforms. Finally, it looks ahead to the future of AI in software testing, discussing emerging trends, ethical considerations, and the evolving role of QE professionals in an AI-first world. With real-world case studies and actionable insights, AI-Driven Software Testing is an essential guide for QE engineers, developers, and tech leaders looking to harness AI for smarter, faster, and more reliable software testing. What you will learn: • What are the key principles of AI/ML-driven quality engineering • What is intelligent test case generation and adaptive test automation • Explore predictive analytics for defect prevention and risk assessment • Understand integration of AI/ML tools in CI/CD pipelines Who this book is for: Quality Engineers looking to enhance software testing with AI-driven techniques. Data Scientists exploring AI applications in software quality assurance and engineering. Software Developers – Engineers seeking to integrate AI/ML into testing and automation workflows.