基于 LLM 的方法自动建立需求与 MBSE 之间的可追溯性

Maria Bonner, Marc Zeller, Gabor Schulz, Ana Savu
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引用次数: 0

摘要

从需求规格到设计和实施的跟踪是安全标准的重要组成部分,因为它可以确保在整个开发过程中实现安全目标。人工追踪整个开发过程中产生的大量工件容易出错,而且耗费大量时间。为了解决这些问题,我们提出了一种工具(Bonner, M.; Zeller, M.; Schulz, G.; Beyer, D.; Olteanu, M., 2023),可以半自动方式在需求和基于模型的系统工程(MBSE)之间建立联系。我们工具的基础算法是基于大型语言模型(LLM)的嵌入式相似性计算和分类方法。为了评估底层算法的性能,我们提出了一个评估方案,在该方案中,我们比较了应用于数据集的不同方法的召回率、精确度和 F2 分数。我们评估的目的是了解 LLM 在不同数据集上自动生成跟踪链接的性能如何。我们的评估结果表明,值得在预处理数据和微调 LLM 方面投入时间,以便为工程师提供更好的建议,从而改进可追溯性流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LLM-based Approach to Automatically Establish Traceability between Requirements and MBSE

Tracing requirements specification to design and implementation is an essential part of safety standards, as it allows to ensure that safety goals are met throughout the development process. Manual tracing numerous artifacts produced throughout the development process is error-prone and takes much time. To address these problems, we proposed a tool (Bonner, M.; Zeller, M.; Schulz, G.; Beyer, D.; Olteanu, M., 2023), which allows to establish links between requirements and Model-Based Systems Engineering (MBSE) in a semi-automatic way. The underlying algorithms of our tool are embedding similarity computation and classification approaches based on Large Language Models (LLMs). To assess the performance of underlying algorithm we propose an evaluation, where we compare the recall, the precision, and the F2 score of different approaches applied to our datasets. The goal of our evaluation is to understand how well LLMs perform in automatically generating trace links on different datasets. Our evaluation shows that it is worth to invest time in preprocessing the data and fine-tuning the LLMs to achieve the better recommendations for engineers, which improves the traceability process.

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