標(biāo)題: Titlebook: Generative AI for Effective Software Development; Anh Nguyen-Duc,Pekka Abrahamsson,Foutse Khomh Book 2024 The Editor(s) (if applicable) an [打印本頁] 作者: 猛烈抨擊 時(shí)間: 2025-3-21 18:24
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書目名稱Generative AI for Effective Software Development網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Generative AI for Effective Software Development讀者反饋學(xué)科排名
作者: connoisseur 時(shí)間: 2025-3-21 20:44 作者: 頭腦冷靜 時(shí)間: 2025-3-22 03:41 作者: 步兵 時(shí)間: 2025-3-22 05:42
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.作者: arbiter 時(shí)間: 2025-3-22 11:30
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作者: 星球的光亮度 時(shí)間: 2025-3-22 13:03
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作者: 星球的光亮度 時(shí)間: 2025-3-22 17:02 作者: aptitude 時(shí)間: 2025-3-23 00:00 作者: Binge-Drinking 時(shí)間: 2025-3-23 01:57 作者: 詩集 時(shí)間: 2025-3-23 06:00
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.作者: 夾克怕包裹 時(shí)間: 2025-3-23 11:11
developers by enabling them to work more efficiently, speed up the learning process, and increase motivation by reducing tedious and repetitive tasks. Moreover, our results indicate a change in teamwork collaboration due to software engineers using GenAI for help instead of asking coworkers, which impacts the learning loop in agile teams.作者: 基因組 時(shí)間: 2025-3-23 17:07
Coefficients for Bivariate Relations,terview with them. Among the lessons learned are that the use of generative AI tools drives the adoption of additional developer tools and that developers intentionally use ChatGPT and Copilot in a complementary manner. We hope that sharing these practical experiences will help other software teams in successfully adopting generative AI tools.作者: conquer 時(shí)間: 2025-3-23 19:33
An Overview on Large Language ModelsLMs and augmented LLMs. Furthermore, we delve into the evaluation of LLM research, introducing benchmark datasets and relevant tools in this context. The chapter concludes by exploring limitations in leveraging LLMs for SE tasks.作者: 陳列 時(shí)間: 2025-3-23 23:16 作者: 不理會(huì) 時(shí)間: 2025-3-24 03:00
Advancing Requirements Engineering Through Generative AI: Assessing the Role of LLMsmprove the efficiency and accuracy of requirements-related tasks. We propose key directions and SWOT analysis for research and development in using LLMs for RE, focusing on the potential for requirements elicitation, analysis, specification, and validation. We further present the results from a preliminary evaluation, in this context.作者: Liability 時(shí)間: 2025-3-24 08:19
Generative AI for Software Development: A Family of Studies on Code Generationiscuss 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.作者: Congruous 時(shí)間: 2025-3-24 12:55 作者: crumble 時(shí)間: 2025-3-24 17:48 作者: 自然環(huán)境 時(shí)間: 2025-3-24 19:49 作者: Aggressive 時(shí)間: 2025-3-25 02:39 作者: 昆蟲 時(shí)間: 2025-3-25 05:08 作者: 芭蕾舞女演員 時(shí)間: 2025-3-25 09:18
ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Softw language models (LLMs) to automate common software engineering activities, such as ensuring code is decoupled from third-party libraries and creating API specifications from lists of requirements. This chapter provides two contributions to research on using LLMs for software engineering. First, it 作者: 老巫婆 時(shí)間: 2025-3-25 14:58 作者: 改正 時(shí)間: 2025-3-25 18:51
Advancing Requirements Engineering Through Generative AI: Assessing the Role of LLMsre requirements. Despite the importance of RE, it remains a challenging process due to the complexities of communication, uncertainty in the early stages, and inadequate automation support. In recent years, large language models (LLMs) have shown significant promise in diverse domains, including nat作者: 粗鄙的人 時(shí)間: 2025-3-25 21:34
Generative AI for Software Development: A Family of Studies on Code Generationare engineering (SE). This chapter explores the benefits and challenges of utilizing generative AI for different activities in the software development cycle that involve code generation. We review different approaches leveraging generative AI, either independently or in combination with traditional作者: Amplify 時(shí)間: 2025-3-26 03:06
BERTVRepair: On the Adoption of CodeBERT for Automated Vulnerability Code Repairnd threats to national security. Traditional methods of detecting and addressing software security issues are often time-consuming and resource-intensive. This research aims to examine the effectiveness of generative-based methods, particularly those leveraging generative (DL) models like CodeBERT, 作者: 激怒 時(shí)間: 2025-3-26 06:35
ChatGPT as a Full-Stack Web Developers of ChatGPT to actually implement a complete system rather than a few code snippets. This chapter reports the firsthand experiences from a graduate-level student project where a real-life software platform for financial sector was implemented from scratch by using ChatGPT for all possible software 作者: 傾聽 時(shí)間: 2025-3-26 09:12 作者: 單色 時(shí)間: 2025-3-26 15:15
How Can Generative AI Enhance Software Management? Is It Better Done than Perfect?rent impact levels if the adaptations are not tailored for the specific teams’ needs and circumstances. For instance, agile developers sometimes oversimplify crucial Agile steps, such as estimating needed effort for a specific task or lack of explicit assessment of the criteria for “Definition of Do作者: oxidant 時(shí)間: 2025-3-26 17:38 作者: 奇怪 時(shí)間: 2025-3-27 00:36 作者: 商品 時(shí)間: 2025-3-27 04:45 作者: Stable-Angina 時(shí)間: 2025-3-27 06:04
Anh Nguyen-Duc,Pekka Abrahamsson,Foutse KhomhProvides 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作者: 針葉 時(shí)間: 2025-3-27 11:41 作者: OUTRE 時(shí)間: 2025-3-27 16:12
https://doi.org/10.1007/978-3-031-55642-5Generative Artificial Inteligence; Software Development; AI-assisted SWE Tools; ChatGPT; Code Generation作者: 混雜人 時(shí)間: 2025-3-27 19:07
https://doi.org/10.1007/978-3-030-87413-1us software engineering (SE) tasks and beyond. This development has influenced the research studies in this domain. This chapter offers an overview of LLMs, delving into relevant background concepts while exploring advanced techniques at the forefront of LLM research. We review various LLM architect作者: electrolyte 時(shí)間: 2025-3-27 23:27
https://doi.org/10.1007/978-3-663-14195-2s domains. These models hold the potential for contributing to software development tasks. This chapter presents a comparative study on the proficiency of ChatGPT and Bard in software development, examining their strengths and weaknesses and synergy in their combined use. To facilitate the compariso作者: 冰河期 時(shí)間: 2025-3-28 04:50 作者: Hay-Fever 時(shí)間: 2025-3-28 06:36 作者: CANE 時(shí)間: 2025-3-28 10:54
Counter Strategies in Global Marketsdvent of GenAI has made these tasks more amenable to automation, thanks to its ability to understand and interpret context effectively. . However, in the context of GenAI, prompt engineering is a critical factor for success. Despite this, we currently lack tools and methods to systematically assess 作者: 政府 時(shí)間: 2025-3-28 15:48
Stylistics and Structural Poetics,re requirements. Despite the importance of RE, it remains a challenging process due to the complexities of communication, uncertainty in the early stages, and inadequate automation support. In recent years, large language models (LLMs) have shown significant promise in diverse domains, including nat作者: CARK 時(shí)間: 2025-3-28 19:18 作者: 填料 時(shí)間: 2025-3-29 02:03 作者: Lethargic 時(shí)間: 2025-3-29 07:05
https://doi.org/10.1007/978-0-387-76874-8s of ChatGPT to actually implement a complete system rather than a few code snippets. This chapter reports the firsthand experiences from a graduate-level student project where a real-life software platform for financial sector was implemented from scratch by using ChatGPT for all possible software 作者: gustation 時(shí)間: 2025-3-29 10:44 作者: 拖債 時(shí)間: 2025-3-29 15:05
rent impact levels if the adaptations are not tailored for the specific teams’ needs and circumstances. For instance, agile developers sometimes oversimplify crucial Agile steps, such as estimating needed effort for a specific task or lack of explicit assessment of the criteria for “Definition of Do作者: 羊齒 時(shí)間: 2025-3-29 18:29
https://doi.org/10.1007/978-3-319-50802-3e projects requires effective communication, collaboration, and decision-making. ChatGPT, a large language model, has the potential to enhance these aspects of Agile project management. In this chapter, we conduct a survey study to understand the perception of Agile project managers about adopting C作者: COLON 時(shí)間: 2025-3-29 22:13 作者: CLIFF 時(shí)間: 2025-3-30 01:16 作者: ARM 時(shí)間: 2025-3-30 07:50 作者: Decongestant 時(shí)間: 2025-3-30 12:17
Introducing Counter-Terrorism Studies,g models. We showed a marginal improvement in accuracy and perplexity. We conclude that using generative-based methods like CodeBERT, with its code embedding extraction and transfer learning approaches, can potentially enhance the process of software vulnerability repair. This research contributes t
