利用生成式人工智能开发软件的未来

IF 2 2区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Automated Software Engineering Pub Date : 2024-03-11 DOI:10.1007/s10515-024-00426-z
Jaakko Sauvola, Sasu Tarkoma, Mika Klemettinen, Jukka Riekki, David Doermann
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引用次数: 0

摘要

生成式人工智能被认为是对软件开发的重大颠覆。事实证明,平台、资源库、云以及工具和流程的自动化可以提高生产率、成本和质量。生成式人工智能凭借其快速扩展的能力,在这一领域向前迈出了一大步。作为一项新的关键使能技术,它可用于多种用途,从创造性层面到替代重复性人工任务。随着大型语言模型(LLM)能力的增强,机会也随之增多。这引起了人们对道德、教育、监管、知识产权甚至犯罪活动的关注。我们分析了生成式人工智能和 LLM 技术在未来软件开发道路上的潜力。我们提出了四种主要情景,模拟了它们之间的过渡轨迹,并对照相关的软件开发操作进行了反思。这项研究的动机很明确:软件开发行业需要新的工具来了解生成式人工智能的潜力、局限性和风险,以及使用它的指导原则。
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Future of software development with generative AI

Generative AI is regarded as a major disruption to software development. Platforms, repositories, clouds, and the automation of tools and processes have been proven to improve productivity, cost, and quality. Generative AI, with its rapidly expanding capabilities, is a major step forward in this field. As a new key enabling technology, it can be used for many purposes, from creative dimensions to replacing repetitive and manual tasks. The number of opportunities increases with the capabilities of large-language models (LLMs). This has raised concerns about ethics, education, regulation, intellectual property, and even criminal activities. We analyzed the potential of generative AI and LLM technologies for future software development paths. We propose four primary scenarios, model trajectories for transitions between them, and reflect against relevant software development operations. The motivation for this research is clear: the software development industry needs new tools to understand the potential, limitations, and risks of generative AI, as well as guidelines for using it.

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来源期刊
Automated Software Engineering
Automated Software Engineering 工程技术-计算机:软件工程
CiteScore
4.80
自引率
11.80%
发文量
51
审稿时长
>12 weeks
期刊介绍: This journal details research, tutorial papers, survey and accounts of significant industrial experience in the foundations, techniques, tools and applications of automated software engineering technology. This includes the study of techniques for constructing, understanding, adapting, and modeling software artifacts and processes. Coverage in Automated Software Engineering examines both automatic systems and collaborative systems as well as computational models of human software engineering activities. In addition, it presents knowledge representations and artificial intelligence techniques applicable to automated software engineering, and formal techniques that support or provide theoretical foundations. The journal also includes reviews of books, software, conferences and workshops.
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