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Communication Dans Un Congrès Année : 2023

Machine Learning Pseudo-Natural Language for Temporal Logic Requirements of Embedded Systems

Ouarda Fenek
  • Fonction : Auteur
  • PersonId : 1297535

Résumé

Requirements formalization is a critical part of any verification methodology for embedded systems like those in the automotive industry. There is a strong tension between techniques that enter requirements as logic- or code-like formal expressions and others that use natural language. The former are much safer but require user training and have low productivity. As a compromise we proposed a context-free grammar for entering real-time system requirements and translating them to temporal logic (TL) unambiously and reversibly. It has been demonstrated on hundreds of examples and became validated by a recent patent. But building or extending the grammar itself requires a precise understanding of the translation rules. To aleviate this new hurdle we have found that neural nets inspired by NLP can learn and then replace the pseudo-English-to-TL translation, and allow extending it without the explicit use of a grammar. The paper explains how we mixed real-life and synthetic datasets and overcame the initial limitations of the neural nets.
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Dates et versions

hal-04252020 , version 1 (20-10-2023)

Identifiants

  • HAL Id : hal-04252020 , version 1

Citer

Gaétan Hains, Ouarda Fenek. Machine Learning Pseudo-Natural Language for Temporal Logic Requirements of Embedded Systems. International Conference on Knowledge and Systems Engineering, IEEE; Academy of Cryptography Techniques, Vietnam, Oct 2023, Hanoi, Vietnam. ⟨hal-04252020⟩

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