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Formalization of Natural Language into PPTL Specification via Neural Machine Translation

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Structured Object-Oriented Formal Language and Method (SOFL+MSVL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13854))

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Abstract

Propositional Projection Temporal Logic (PPTL) has been widely used in formal verification, and its expressiveness is suitable for the description of security requirements. However, the expression and application of temporal logic formulas rely on a strong mathematical background, which is difficult for non-domain experts, thus bridging the chasm between natural language descriptions and formal languages is urgently needed. This paper proposes an innovative architecture for neural machine automatic translation named NL2PPTL, which transforms natural language into PPTL specification via utilizing data preprocessing, encoder-decoder network and stack sequentially. To evaluate the performance of our method, the experimental verification is realized on real datasets. The experiment conducted shows that our method has effectiveness on temporal logic specification generation.

Supported by National Natural Science Foundation of China (61972301, 61672403), Key Research and Development Program of Shaanxi Province of China (2020GY-043), Shaanxi Innovative Research Team for Key Science and Technology (2019TD-001), and Xi’an Science and Technology Project (22GXFW0025).

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Notes

  1. 1.

    https://github.com/luoluohuaci/NL2PPTL_NMT.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, China

    Chunyi Li, Jiajun Chang, Xiaobing Wang, Liang Zhao & Wenjie Mao

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  1. Chunyi Li
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  2. Jiajun Chang
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  3. Xiaobing Wang
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  4. Liang Zhao
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Correspondence to Xiaobing Wang .

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Editors and Affiliations

  1. Hiroshima University, Hiroshima, Japan

    Shaoying Liu

  2. Xidian University, Xi’an, China

    Zhenhua Duan

  3. Hiroshima University, Hiroshima, Japan

    Ai Liu

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Li, C., Chang, J., Wang, X., Zhao, L., Mao, W. (2023). Formalization of Natural Language into PPTL Specification via Neural Machine Translation. In: Liu, S., Duan, Z., Liu, A. (eds) Structured Object-Oriented Formal Language and Method. SOFL+MSVL 2022. Lecture Notes in Computer Science, vol 13854. Springer, Cham. https://doi.org/10.1007/978-3-031-29476-1_7

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  • DOI: https://doi.org/10.1007/978-3-031-29476-1_7

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