iStar Extension for Machine Learning Requirements

This repository contains an extension of the iStar framework for capturing Machine Learning (ML) requirements, developed following the PRISE methodology. The project provides a comprehensive metamodel that bridges the gap between domain experts and ML developers, facilitating the translation of high-level requirements into specific ML implementations. This work is described in the paper:

"An extension of iStar for Machine Learning requirements by following the PRISE methodology"

Authors: Jose M. Barrera, Alejandro Reina-Reina, Ana Lavalle, Alejandro Maté, Juan Trujillo. Published in: Computer Standards & Interfaces, 88, 103806 (2024) DOI: (10.1007/s13042-022-01583-x)

System Requirements

To use or implement the metamodel, you will need the following tools and dependencies:

• Eclipse Modeling Framework (ECORE).
• Basic knowledge of the iStar framework and PRISE methodology.

Citation

If you use this repository in your research or work, please cite the original paper:

ISO 690 Format: BARRERA, Jose M., et al. An extension of iStar for Machine Learning requirements by following the PRISE methodology. Computer Standards & Interfaces, 2024, vol. 88, p. 103806.

BibTeX:

@article{barrera2024malistar,
  title={An extension of iStar for Machine Learning requirements by following the PRISE methodology},
  author={Barrera, Jose M and Reina-Reina, Alejandro and Lavalle, Ana and Mat{\'e}, Alejandro and Trujillo, Juan},
  journal={Computer Standards \& Interfaces},
  volume={88},
  pages={103806},
  year={2024},
  publisher={Elsevier}
}