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Ontological Evolutionary Encoding to Bridge Machine Learning and Conceptual Models: Approach and Industrial Evaluation

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Conceptual Modeling (ER 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10650))

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Abstract

In this work, we propose an evolutionary ontological encoding approach to enable Machine Learning techniques to be used to perform Software Engineering tasks in models. The approach is based on a domain ontology to encode a model and on an Evolutionary Algorithm to optimize the encoding. As a result, the encoded model that is returned by the approach can then be used by Machine Learning techniques to perform Software Engineering tasks such as concept location, traceability link retrieval, reuse, impact analysis, etc. We have evaluated the approach with an industrial case study to recover the traceability link between the requirements and the models through a Machine Learning technique (RankBoost). Our results in terms of recall, precision, and the combination of both (F-measure) show that our approach outperforms the baseline (Latent Semantic Indexing). We also performed a statistical analysis to assess the magnitude of the improvement.

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Acknowledgments

This work has been developed with the financial support of the Spanish Ministry of Economy and Competitiveness under the project TIN2016-80811-P and co-financed with ERDF. We also thank both ITEA3 15010 REVaMP\(^2\) Project and MINECO TIN2015-64397-R VARIAMOS Project.

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Authors and Affiliations

  1. Centro de Investigación en Métodos de Producción de Software, Universitat Politècnica de València, Camino de Vera, s/n, 46022, Valencia, Spain

    Ana C. Marcén

  2. SVIT Research Group, Universidad San Jorge, Autovía A-23 Zaragoza-Huesca Km. 299, 50830, Zaragoza, Spain

    Ana C. Marcén, Francisca Pérez & Carlos Cetina

Authors
  1. Ana C. Marcén
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  2. Francisca Pérez
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  3. Carlos Cetina
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Corresponding author

Correspondence to Ana C. Marcén .

Editor information

Editors and Affiliations

  1. University of Klagenfurt, Klagenfurt, Austria

    Heinrich C. Mayr

  2. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Giancarlo Guizzardi

  3. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

  4. Valencia University of Technology, Valencia, Spain

    Oscar Pastor

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Marcén, A.C., Pérez, F., Cetina, C. (2017). Ontological Evolutionary Encoding to Bridge Machine Learning and Conceptual Models: Approach and Industrial Evaluation. In: Mayr, H., Guizzardi, G., Ma, H., Pastor, O. (eds) Conceptual Modeling. ER 2017. Lecture Notes in Computer Science(), vol 10650. Springer, Cham. https://doi.org/10.1007/978-3-319-69904-2_37

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  • DOI: https://doi.org/10.1007/978-3-319-69904-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69903-5

  • Online ISBN: 978-3-319-69904-2

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