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Faced with the growing complexity of modern software systems, there is an urgent demand for improved development methodologies and tools. Although model-driven engineering has significantly improved software quality and productivity in recent years, it is confronted with the growing complexity of model specification and management. Recently, a new concept of low-modeling has been introduced, in which models are treated like code. In other words, if we have “low code”, we can also have “low modeling”. In this paper, we present an approach combining the concepts of natural language processing (NLP) and LLMs to automatically generate models

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Low-Modeling: Leveraging Transformer-Based Models for Automated Model Generation

  • 会议论文

  • 发表日期：

    2025年

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  • Nihal Salih,
  • Mahmoud El Hamlaoui,
  • Tarik Fissaa

摘要

Faced with the growing complexity of modern software systems, there is an urgent demand for improved development methodologies and tools. Although model-driven engineering has significantly improved software quality and productivity in recent years, it is confronted with the growing complexity of model specification and management. Recently, a new concept of low-modeling has been introduced, in which models are treated like code. In other words, if we have “low code”, we can also have “low modeling”. In this paper, we present an approach combining the concepts of natural language processing (NLP) and LLMs to automatically generate models