Assessing the Performance of Automated Model Extraction Rules

Jorge Echeverría (), Francisca Pérez (), Óscar Pastor () and Carlos Cetina ()
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Jorge Echeverría: Universidad San Jorge
Francisca Pérez: Universidad San Jorge
Óscar Pastor: Universitat Politècnica de València
Carlos Cetina: Universidad San Jorge

A chapter in Advances in Information Systems Development, 2018, pp 33-49 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-319-74817-7_3

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DOI: 10.1007/978-3-319-74817-7_3

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