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Titlebook: Generative AI for Effective Software Development; Anh Nguyen-Duc,Pekka Abrahamsson,Foutse Khomh Book 2024 The Editor(s) (if applicable) an

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書目名稱Generative AI for Effective Software Development
編輯Anh Nguyen-Duc,Pekka Abrahamsson,Foutse Khomh
視頻videohttp://file.papertrans.cn/383/382340/382340.mp4
概述Provides a comprehensive, empirically based exploration of how Generative AI reshapes the landscape of SW development.Presents case studies, surveys, and interviews how Generative AI is applied in var
圖書封面Titlebook: Generative AI for Effective Software Development; Anh Nguyen-Duc,Pekka Abrahamsson,Foutse Khomh Book 2024 The Editor(s) (if applicable) an
描述.This book provides a comprehensive, empirically grounded exploration of how Generative AI is reshaping the landscape of software development. It emphasizes the empirical evaluation of Generative AI tools in real-world scenarios, offering insights into their practical efficacy, limitations, and impact. By presenting case studies, surveys, and interviews from various software development contexts, the book offers a global perspective on the integration of Generative AI, highlighting how these advanced tools are adapted to and influence diverse cultural, organizational, and technological environments...This book is structured to provide a comprehensive understanding of Generative AI and its transformative impact on the field of software engineering. The book is divided into five parts, each focusing on different aspects of Generative AI in software development. As an introduction, Part 1 presents the fundamentals of Generative AI adoption. Part 2 is a collection of empirical studies and delves into the practical aspects of integrating Generative AI tools in software engineering, with a focus on patterns, methodologies, and comparative analyses. Next, Part 3 presents case studies that
出版日期Book 2024
關(guān)鍵詞Generative Artificial Inteligence; Software Development; AI-assisted SWE Tools; ChatGPT; Code Generation
版次1
doihttps://doi.org/10.1007/978-3-031-55642-5
isbn_softcover978-3-031-55641-8
isbn_ebook978-3-031-55642-5
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Softwprovides a catalog of patterns for software engineering that classifies patterns according to the types of problems they solve. Second, it explores several prompt patterns that have been applied to improve requirements elicitation, rapid prototyping, code quality, deployment, and testing.
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Book 2024asizes the empirical evaluation of Generative AI tools in real-world scenarios, offering insights into their practical efficacy, limitations, and impact. By presenting case studies, surveys, and interviews from various software development contexts, the book offers a global perspective on the integr
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surveys, and interviews how Generative AI is applied in var.This book provides a comprehensive, empirically grounded exploration of how Generative AI is reshaping the landscape of software development. It emphasizes the empirical evaluation of Generative AI tools in real-world scenarios, offering i
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Countering the Recruitment Pitch,iscuss the potential pitfalls of using generative AI to perform such SE tasks, as well as the quality of the code generated by these models. Finally, we explore research opportunities in harnessing generative AI, with a particular emphasis on tasks that require code generation.
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