A business subject who wishes to enter an established technological market is required to accurately analyse the features of the products of the different competitors. Such features are normally accessible through natural language (NL) brochures, or NL Web pages, which describe the products to potential customers. Building a feature model that hierarchically summarises the different features available in competing products can bring relevant benefits in market analysis. A company can easily visualise existing features, and reason about aspects that are not covered by the available solutions. However, designing a feature model starting from publicly available documents of existing products is a time consuming and error-prone task. In this paper, we present two tools, namely Commonality Mining Tool (CMT) and Feature Diagram Editor (FDE), which can jointly support the feature model definition process. CMT allows mining common and variant features from NL descriptions of existing products, by leveraging a natural language processing (NLP) approach based on contrastive analysis, which allows identifying domain-relevant terms from NL documents. FDE takes the commonalities and variabilities extracted by CMT, and renders them in a visual form. Moreover, FDE allows the graphical design and refinement of the final feature model, by means of an intuitive GUI

CMT and FDE: tools to bridge the gap between natural language documents and feature diagrams

Ferrari A;Spagnolo G O;Gnesi S;Dell'Orletta F
2015

Abstract

A business subject who wishes to enter an established technological market is required to accurately analyse the features of the products of the different competitors. Such features are normally accessible through natural language (NL) brochures, or NL Web pages, which describe the products to potential customers. Building a feature model that hierarchically summarises the different features available in competing products can bring relevant benefits in market analysis. A company can easily visualise existing features, and reason about aspects that are not covered by the available solutions. However, designing a feature model starting from publicly available documents of existing products is a time consuming and error-prone task. In this paper, we present two tools, namely Commonality Mining Tool (CMT) and Feature Diagram Editor (FDE), which can jointly support the feature model definition process. CMT allows mining common and variant features from NL descriptions of existing products, by leveraging a natural language processing (NLP) approach based on contrastive analysis, which allows identifying domain-relevant terms from NL documents. FDE takes the commonalities and variabilities extracted by CMT, and renders them in a visual form. Moreover, FDE allows the graphical design and refinement of the final feature model, by means of an intuitive GUI
Campo DC Valore Lingua
dc.authority.orgunit Istituto di linguistica computazionale "Antonio Zampolli" - ILC -
dc.authority.orgunit Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI -
dc.authority.people Ferrari A it
dc.authority.people Spagnolo G O it
dc.authority.people Gnesi S it
dc.authority.people Dell'Orletta F it
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dc.date.issued 2015 -
dc.description.abstracteng A business subject who wishes to enter an established technological market is required to accurately analyse the features of the products of the different competitors. Such features are normally accessible through natural language (NL) brochures, or NL Web pages, which describe the products to potential customers. Building a feature model that hierarchically summarises the different features available in competing products can bring relevant benefits in market analysis. A company can easily visualise existing features, and reason about aspects that are not covered by the available solutions. However, designing a feature model starting from publicly available documents of existing products is a time consuming and error-prone task. In this paper, we present two tools, namely Commonality Mining Tool (CMT) and Feature Diagram Editor (FDE), which can jointly support the feature model definition process. CMT allows mining common and variant features from NL descriptions of existing products, by leveraging a natural language processing (NLP) approach based on contrastive analysis, which allows identifying domain-relevant terms from NL documents. FDE takes the commonalities and variabilities extracted by CMT, and renders them in a visual form. Moreover, FDE allows the graphical design and refinement of the final feature model, by means of an intuitive GUI -
dc.description.affiliations CNR-ISTI, Pisa, italy; CNR-ISTI, Pisa, italy; CNR-ISTI, Pisa, italy; CNR-ISTI, Pisa, italy; -
dc.description.allpeople Ferrari, A; Spagnolo, G O; Gnesi, S; Dell'Orletta, F -
dc.description.allpeopleoriginal Ferrari A.; Spagnolo G. O.; Gnesi S.; Dell'Orletta F. -
dc.description.fulltext partially_open en
dc.description.note Progetto Model-Based Social Learning for Public Administrations - Acronimo LEARN PAD - Grant agreement619583 - Tipo ProgettoEU_FP7 - -
dc.description.numberofauthors 4 -
dc.identifier.doi 10.1145/2791060.2791117 -
dc.identifier.isbn 978-1-4503-3613-0 -
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dc.relation.conferencedate 20-24/07/2015 -
dc.relation.conferencename 19th International Conference on Software Product Line -
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dc.relation.firstpage 402 -
dc.relation.lastpage 410 -
dc.relation.projectAcronym LEARN PAD -
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dc.relation.projectAwardTitle Model-Based Social Learning for Public Administrations -
dc.relation.projectFunderName - en
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dc.subject.keywords Software Product Lines -
dc.subject.keywords Variability Mining -
dc.subject.keywords Tools -
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dc.title CMT and FDE: tools to bridge the gap between natural language documents and feature diagrams en
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scopus.description.abstracteng A business subject who wishes to enter an established technological market is required to accurately analyse the features of the products of the different competitors. Such features are normally accessible through natural language (NL) brochures, or NL Web pages, which describe the products to potential customers. Building a feature model that hierarchically summarises the different features available in competing products can bring relevant benefits in market analysis. A company can easily visualise existing features, and reason about aspects that are not covered by the available solutions. However, designing a feature model starting from publicly available documents of existing products is a time consuming and error-prone task. In this paper, we present two tools, namely Commonality Mining Tool (CMT) and Feature Diagram Editor (FDE), which can jointly support the feature model definition process. CMT allows mining common and variant features from NL descriptions of existing products, by leveraging a natural language processing (NLP) approach based on contrastive analysis, which allows identifying domain-relevant terms from NL documents. FDE takes the commonalities and variabilities extracted by CMT, and renders them in a visual form. Moreover, FDE allows the graphical design and refinement of the final feature model, by means of an intuitive GUI. *
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scopus.title CMT and FDE: Tools to bridge the gap between natural language documents and feature diagrams *
scopus.titleeng CMT and FDE: Tools to bridge the gap between natural language documents and feature diagrams *
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