Assessing the Performance of Automated Model Extraction Rules

  • Jorge Echeverría
  • Francisca Pérez
  • Óscar Pastor
  • Carlos Cetina
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 26)

Abstract

Automated Model Extraction Rules take as input requirements (in natural language) to generate domain models. Despite the existing work on these rules, there is a lack of evaluations in industrial settings. To address this gap, we conduct an evaluation in an industrial context, reporting the extraction rules that are triggered to create a model from requirements and their frequency. We also assess the performance in terms of recall, precision and F-measure of the generated model compared to the models created by domain experts of our industrial partner. Results enable us to identify new research directions to push forward automated model extraction rules: the inclusion of new knowledge sources as input for the extraction rules, and the development of specific experiments to evaluate the understanding of the generated models.

Keywords

Conceptual models Natural language requirements Model extraction 

Notes

Acknowledgements

This work has been partially supported by the Ministry of Economy and Competitiveness (MINECO) through the Spanish National R+D+i Plan and ERDF funds under the project Model-Driven Variability Extraction for Software Product Line Adoption (TIN2015-64397-R).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jorge Echeverría
    • 1
  • Francisca Pérez
    • 1
  • Óscar Pastor
    • 2
  • Carlos Cetina
    • 1
  1. 1.Universidad San JorgeZaragozaSpain
  2. 2.Universitat Politècnica de ValènciaValenciaSpain

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