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Model-Driven Engineering (MDE) enhances software development by utilizing abstract system models. However, the existence of anomalous models can compromise system quality and reliability. This paper introduces an unsupervised machine learning approach that employs the Isolation Forest algorithm to detect anomalies in software models without requiring labeled data. The method achieves a 99% accuracy on Ecore models and 98% accuracy on UML models, demonstrating its effectiveness in identifying anomalous models and enhancing the robustness of model development.

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Suspicious Model Detection to Improve Model-Based System Development

  • 会议论文

  • 发表日期：

    2026年

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  • Marwareed Rehman,
  • Muhammad Waseem Anwar,
  • Farooque Azam,
  • Saliha Ejaz

摘要

Model-Driven Engineering (MDE) enhances software development by utilizing abstract system models. However, the existence of anomalous models can compromise system quality and reliability. This paper introduces an unsupervised machine learning approach that employs the Isolation Forest algorithm to detect anomalies in software models without requiring labeled data. The method achieves a 99% accuracy on Ecore models and 98% accuracy on UML models, demonstrating its effectiveness in identifying anomalous models and enhancing the robustness of model development.