Requirement Mining for Model-Based Product Design

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

Requirement mining for model-based product design

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

Requirement mining for model-based product design

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

Requirement Mining for Model-Based Product Design

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

Knowledge data discovery and data mining in a design environment

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development