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 |
| dc.authority.project | Model-Based Social Learning for Public Administrations | - |
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| dc.contributor.appartenenza | Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI | * |
| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
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| dc.date.accessioned | 2024/02/21 07:14:35 | - |
| dc.date.available | 2024/02/21 07:14:35 | - |
| 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 | - |
| dc.identifier.scopus | 2-s2.0-84982833628 | - |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/310030 | - |
| dc.identifier.url | http://dl.acm.org/citation.cfm?doid=2791060.2791117 | - |
| dc.language.iso | eng | - |
| dc.relation.conferencedate | 20-24/07/2015 | - |
| dc.relation.conferencename | 19th International Conference on Software Product Line | - |
| dc.relation.conferenceplace | Nashville, TN, USA | - |
| dc.relation.firstpage | 402 | - |
| dc.relation.lastpage | 410 | - |
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| dc.relation.projectAwardTitle | Model-Based Social Learning for Public Administrations | - |
| dc.relation.projectFunderName | - | en |
| dc.relation.projectFundingStream | FP7 | - |
| dc.subject.keywords | Software Product Lines | - |
| dc.subject.keywords | Variability Mining | - |
| dc.subject.keywords | Tools | - |
| dc.subject.singlekeyword | Software Product Lines | * |
| dc.subject.singlekeyword | Variability Mining | * |
| dc.subject.singlekeyword | Tools | * |
| 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. | * |
| scopus.description.allpeopleoriginal | Ferrari A.; Spagnolo G.O.; Gnesi S.; Dell'orletta F. | * |
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| Appare nelle tipologie: | 04.01 Contributo in Atti di convegno | |
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