This paper introduces ReqH, an innovative tool designed to streamline the translation of natural language requirements into Property Specification Patterns. The tool leverages the capabilities of Large Language Models, which are renowned for their ability to comprehend and generate human-like text. ReqH aims to address the challenges of translating informal requirements into formal specifications, a process that is crucial in industrial contexts, particularly within safety and security-critical domains which demand rigorous formalisation to ensure the reliability and security of systems. We present some preliminary results from evaluating our methodology on a dataset of semi-automatically generated automotive requirements. The findings indicate that Large Language Models, when applied to this translation process, show significant potential for improving the accuracy and efficiency of requirement specification.
Translating Requirements in Property Specification Patterns using LLMs / Guidotti, D.; Pandolfo, L.; Fanni, T.; Zedda, K.; Pulina, L.. - 3883:(2024), pp. 176-188. ( 1st International Workshop on Artificial Intelligence for Climate Change, 12th Italian Workshop on Planning and Scheduling, 31st RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, and SPIRIT Workshop on Strategies, Prediction, Interaction, and Reasoning in Italy, AI4CC-IPS-RCRA-SPIRIT 2024 ita 2024).
Translating Requirements in Property Specification Patterns using LLMs
Guidotti D.;Pandolfo L.;Fanni T.;Pulina L.
2024-01-01
Abstract
This paper introduces ReqH, an innovative tool designed to streamline the translation of natural language requirements into Property Specification Patterns. The tool leverages the capabilities of Large Language Models, which are renowned for their ability to comprehend and generate human-like text. ReqH aims to address the challenges of translating informal requirements into formal specifications, a process that is crucial in industrial contexts, particularly within safety and security-critical domains which demand rigorous formalisation to ensure the reliability and security of systems. We present some preliminary results from evaluating our methodology on a dataset of semi-automatically generated automotive requirements. The findings indicate that Large Language Models, when applied to this translation process, show significant potential for improving the accuracy and efficiency of requirement specification.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


