第二届基于自然语言的软件工程研讨会(NLBSE 2023)摘要

Sebastiano Panichella, Andrea Di Sorbo
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摘要

自然语言处理(NLP)涉及对人类语言的自动分析和处理。这包括可以分析人类创造的文本的算法和可以生成看起来自然的文本的算法。如今,NLP方法在软件开发的各个方面变得越来越普遍。实际上,在整个软件开发生命周期中,会产生大量的自然语言工件。因此,基于nlp的方法和工具的存在为提高软件工程师、过程和产品的效率和生产力开辟了可能性。第二届基于自然语言的软件工程研讨会(NLBSE)于2023年与第45届国际软件工程会议(ICSE 2023)同时举行,研究界参与了关于这些方法的讨论。本次会议汇集了来自NLP和软件工程领域的研究人员和实践者,交流经验,建立未来的研究方向,并促进采用NLP技术和工具来解决软件工程中的特定挑战。在本文中,我们介绍了第二版研讨会的总结,其中包括三篇全文论文,四篇简短/立场论文,五篇工具竞赛/演示论文,两篇主题演讲(\自动化Bug管理:反思&;David Lo的《未来之路》和Albert Ziegler的《大型语言模型应用的趋势和机遇:追求最大效果》),随后是NLBSE参与者之间的广泛讨论。更多详细信息请访问https://nlbse2023.github.io/index。超文本标记语言
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Summary of the 2nd Natural Language-based Software Engineering Workshop (NLBSE 2023)
Natural language processing (NLP) involves the automated anal- ysis and manipulation of human language. This includes algo- rithms that can analyze text created by humans and algorithms that can generate text that appears natural. Nowadays, NLP methods are becoming increasingly prevalent to enhance various aspects of software development. Indeed, throughout the software development lifecycle, numerous natural language artifacts are produced. Therefore, the existence of NLP-based approaches and tools has opened up possibilities for improving the e ectiveness and productivity of software engineers, processes, and products. The second edition of the Natural Language-Based Software Engi- neering Workshop (NLBSE) took place in 2023 alongside the 45th International Conference on Software Engineering (ICSE 2023), where the research community engaged in discussions about these approaches. This event brought together researchers and practi- tioners from the elds of NLP and software engineering to ex- change experiences, establish future research directions, and pro- mote the adoption of NLP techniques and tools in tackling chal- lenges speci c to software engineering. In this paper, we present a summary of the 2nd edition of the workshop, which comprised three full papers, four short/position papers, ve tool competi- tion/demonstration papers, two keynote talks (\Automated Bug Management: Re ections & the Road Ahead" by David Lo and \Trends and Opportunities in the Application of Large Language Models: the Quest for Maximum E ect" by Albert Ziegler), fol- lowed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2023.github.io/index. html
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