Article: Requirement mining for model-based product design Journal: International Journal of Product Lifecycle Management (IJPLM) 2016 Vol.9 No.4 pp.305 - 332 Abstract: PLM software applications should enable engineers to develop and manage requirements throughout the product's lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate. Inderscience Publishers - linking academia, business and industry through research

Title: Requirement mining for model-based product design

Authors: Romain Pinquié; Philippe Véron; Frédéric Segonds; Nicolas Croué

Addresses: Laboratoire des Sciences de l'Information et des Systèmes, UMR CNRS 7296, Arts et Métiers ParisTech, 2, cours des Arts et Métiers, 13617 Aix-en-Provence, France ' Laboratoire des Sciences de l'Information et des Systèmes, UMR CNRS 7296, Arts et Métiers ParisTech, 2, cours des Arts et Métiers, 13617 Aix-en-Provence, France ' Laboratoire Conception de Produits et Innovation, Arts et Métiers ParisTech, 151, Boulevard de l'Hôpital, 75013 Paris, France ' Keonys, 5, avenue de l'Escadrille Normandie Niemen, 31700 Blagnac, France

Abstract: PLM software applications should enable engineers to develop and manage requirements throughout the product's lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate.

Keywords: requirements mining; unstructured; requirements extraction; classification; natural language processing; NLP; supervised learning; machine learning; model-based product design; PLM; product lifecycle management; requirements management; text classifiers.

DOI: 10.1504/IJPLM.2016.080983

International Journal of Product Lifecycle Management, 2016 Vol.9 No.4, pp.305 - 332

Received: 23 Feb 2016
Accepted: 12 Sep 2016
Published online: 13 Dec 2016 *

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Title: Requirement mining for model-based product design

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