Towards Automated Domain Model Extraction from Source Code using Heuristics and Open-Source LLMs

Abstract

Large language models (LLMs) have recently shown strong capabilities for code understanding, making them promising for reverse engineering domain models from source code. However, state-ofthe- art proprietary LLMs cannot be used in many industrial contexts due to privacy and confidentiality constraints, while compact open-source LLMs that can run locally are limited by their context window and cannot process large code bases directly. In this paper, we propose an automated approach to extract do

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@misc{mancas2026automated,
  title = {{Towards Automated Domain Model Extraction from Source Code using Heuristics and Open-Source LLMs}},
  author = {Alessandra Mancas and Mounir Ammam and Hyacinth Ali and Kevin Delcourt and H. Sahraoui},
  year = {2026},
  month = aug,
  eprint = {2608.12228},
  archivePrefix = {arXiv},
  url = {https://www.semanticscholar.org/paper/440cae9d353d5f570773f5158d771cb6d1266cf2}
}