AutoMTLSpec: Learning to Generate MTL Specifications from Natural Language Contracts

• Published in: Proceedings (International Conference on Engineering of Complex Computer Systems. Online) pp. 71 - 80
• Main Authors: Ge, Ning, Yang, Jinwen, Yu, Tianyu, Liu, Wei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.06.2023
• Subjects: Deep learning; formal specification generation; Law; Manuals; Measurement; metric temporal property; Natural languages; smart legal contract; Software; time-constrained functional requirements; Writing
• ISSN: 2770-8535
• DOI: 10.1109/ICECCS59891.2023.00018

A smart legal contract is a legally binding contract in which some or all of the contractual obligations are defined and performed automatically by a computer program. As its software requirement, the legal contract is composed of legal clauses expressing the execution logic and time constraints between events in natural language. When formally verifying a smart legal contract to ensure the requirements' conformance, it is necessary to translate the time-constrained functional requirements (TFRs) into property specifications like Metric temporal logic (MTL) as the input of a model checker. Instead of costly and error-prone manual writing, this work automates the TFR detection and the specification generation using deep learning, named AutoMTL-Spec. We separate the MTL specification generation approach into four tasks: TFR detection, intermediate representation structure extraction, event sequence/time point extraction, and MTL generation, respectively. We construct a dataset including 43 contracts of four categories, 4608 terms, and 277 TFRs. The experimental results showed that all three models significantly outperform the baselines. Most of the indicators of the three learning tasks reached near to or more than 90%.