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From English to Signal Temporal Logic
arXiv - CS - Software Engineering Pub Date : 2021-09-21 , DOI: arxiv-2109.10294
Jie He, Ezio Bartocci, Dejan Ničković, Haris Isakovic, Radu Grosu

Formal methods provide very powerful tools and techniques for the design and analysis of complex systems. Their practical application remains however limited, due to the widely accepted belief that formal methods require extensive expertise and a steep learning curve. Writing correct formal specifications in form of logical formulas is still considered to be a difficult and error prone task. In this paper we propose DeepSTL, a tool and technique for the translation of informal requirements, given as free English sentences, into Signal Temporal Logic (STL), a formal specification language for cyber-physical systems, used both by academia and advanced research labs in industry. A major challenge to devise such a translator is the lack of publicly available informal requirements and formal specifications. We propose a two-step workflow to address this challenge. We first design a grammar-based generation technique of synthetic data, where each output is a random STL formula and its associated set of possible English translations. In the second step, we use a state-of-the-art transformer-based neural translation technique, to train an accurate attentional translator of English to STL. The experimental results show high translation quality for patterns of English requirements that have been well trained, making this workflow promising to be extended for processing more complex translation tasks.

中文翻译:

从英语到信号时序逻辑

形式化方法为复杂系统的设计和分析提供了非常强大的工具和技术。然而,它们的实际应用仍然有限,因为人们普遍认为形式方法需要广泛的专业知识和陡峭的学习曲线。以逻辑公式的形式编写正确的正式规范仍然被认为是一项困难且容易出错的任务。在本文中,我们提出了 DeepSTL,这是一种工具和技术,用于将作为免费英语句子给出的非正式需求翻译成信号时间逻辑 (STL),一种用于网络物理系统的正式规范语言,被学术界和高级研究实验室使用在工业中。设计这样一个翻译器的一个主要挑战是缺乏公开可用的非正式要求和正式规范。我们提出了一个两步工作流程来应对这一挑战。我们首先设计了一种基于语法的合成数据生成技术,其中每个输出都是一个随机的 STL 公式及其相关的一组可能的英语翻译。在第二步中,我们使用最先进的基于转换器的神经翻译技术来训练一个准确的英语到 STL 的注意力翻译器。实验结果表明,经过良好训练的英语要求模式的翻译质量很高,这使得该工作流程有望扩展以处理更复杂的翻译任务。我们使用最先进的基于转换器的神经翻译技术,来训练一个准确的英语到 STL 的注意力翻译器。实验结果表明,经过良好训练的英语要求模式的翻译质量很高,这使得该工作流程有望扩展以处理更复杂的翻译任务。我们使用最先进的基于转换器的神经翻译技术,来训练一个准确的英语到 STL 的注意力翻译器。实验结果表明,经过良好训练的英语要求模式的翻译质量很高,这使得该工作流程有望扩展以处理更复杂的翻译任务。
更新日期:2021-09-22
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