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arXiv 2304.09181cs.SEcs.AI
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基于大语言模型的软件规格自动综合

Large Language Models Based Automatic Synthesis of Software Specifications

Texas A&M University(德州农工大学);Intel Labs(英特尔实验室)

Shantanu Mandal, Adhrik Chethan, Vahid Janfaza, S M Farabi Mahmud, Todd A Anderson, Javier Turek, Jesmin Jahan Tithi, Abdullah Muzahid

AI总结:

本文提出SpecSyn框架,首次利用大语言模型以序列到序列方式从自然语言文本中端到端自动综合软件规格,F1分数比现有最佳工具提升21%。

摘要:

软件配置在决定软件系统行为方面起着至关重要的作用。为了确保安全且无错误的运行,有必要识别正确的配置及其有效边界和规则,这些通常被称为软件规格。随着软件系统复杂性和规模的不断增长,为确保正确运行所需的配置数量和相关规格可能变得庞大,并且极难手动处理。由于软件开发的快速节奏,正确软件规格往往未能在软件内部得到充分检查或验证。相反,它们经常在各种外部来源中被讨论和记录,包括软件手册、代码注释和在线讨论论坛。因此,由于缺乏清晰性、组织性以及一个集中统一的查阅来源,系统管理员很难了解配置的正确规格。为了应对这一挑战,我们提出了SpecSyn框架,该框架利用最先进的大语言模型从自然语言来源自动综合软件规格。我们的方法将软件规格综合形式化为序列到序列学习问题,并研究了从大规模上下文文本中提取规格的问题。这是首个使用大语言模型从自然语言文本进行端到端规格综合的工作。实证结果表明,我们的系统在F1分数上比先前最先进的规格综合工具高出21%,并且能够从单个句子以及多个句子中发现规格。

英文摘要:

Software configurations play a crucial role in determining the behavior of software systems. In order to ensure safe and error-free operation, it is necessary to identify the correct configuration, along with their valid bounds and rules, which are commonly referred to as software specifications. As software systems grow in complexity and scale, the number of configurations and associated specifications required to ensure the correct operation can become large and prohibitively difficult to manipulate manually. Due to the fast pace of software development, it is often the case that correct software specifications are not thoroughly checked or validated within the software itself. Rather, they are frequently discussed and documented in a variety of external sources, including software manuals, code comments, and online discussion forums. Therefore, it is hard for the system administrator to know the correct specifications of configurations due to the lack of clarity, organization, and a centralized unified source to look at. To address this challenge, we propose SpecSyn a framework that leverages a state-of-the-art large language model to automatically synthesize software specifications from natural language sources. Our approach formulates software specification synthesis as a sequence-to-sequence learning problem and investigates the extraction of specifications from large contextual texts. This is the first work that uses a large language model for end-to-end specification synthesis from natural language texts. Empirical results demonstrate that our system outperforms prior the state-of-the-art specification synthesis tool by 21% in terms of F1 score and can find specifications from single as well as multiple sentences.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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