Automated Extraction of Product Comparison Matrices From Informal Product Descriptions

Sana Ben Nasr, Guillaume Bécan, Mathieu Acher, João Bosco Ferreira Filho, Nicolas Sannier, Benoit Baudry, Jean-Marc Davril

Résultats de recherche: Contribution à un journal/une revueArticle

Résumé

Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product. In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users.

langueAnglais
Pages82-103
Nombre de pages22
journalJournal of Systems and Software
Volume124
Les DOIs
étatPublié - 1 févr. 2017

Empreinte digitale

Pulse code modulation
Managers
Specifications

mots-clés

    Citer ceci

    Ben Nasr, Sana ; Bécan, Guillaume ; Acher, Mathieu ; Filho, João Bosco Ferreira ; Sannier, Nicolas ; Baudry, Benoit ; Davril, Jean-Marc. / Automated Extraction of Product Comparison Matrices From Informal Product Descriptions. Dans: Journal of Systems and Software. 2017 ; Vol 124. p. 82-103.
    @article{7802dad53da24df0adc68bc4084c36e5,
    title = "Automated Extraction of Product Comparison Matrices From Informal Product Descriptions",
    abstract = "Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product. In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users.",
    keywords = "Feature mining, Product comparison matrices, Reverse engineering, Software product lines, Variability mining",
    author = "{Ben Nasr}, Sana and Guillaume B{\'e}can and Mathieu Acher and Filho, {Jo{\~a}o Bosco Ferreira} and Nicolas Sannier and Benoit Baudry and Jean-Marc Davril",
    year = "2017",
    month = "2",
    day = "1",
    doi = "10.1016/j.jss.2016.11.018",
    language = "English",
    volume = "124",
    pages = "82--103",
    journal = "Journal of Systems and Software",
    issn = "0164-1212",
    publisher = "Elsevier Inc.",

    }

    Automated Extraction of Product Comparison Matrices From Informal Product Descriptions. / Ben Nasr, Sana; Bécan, Guillaume; Acher, Mathieu; Filho, João Bosco Ferreira; Sannier, Nicolas; Baudry, Benoit; Davril, Jean-Marc.

    Dans: Journal of Systems and Software, Vol 124, 01.02.2017, p. 82-103.

    Résultats de recherche: Contribution à un journal/une revueArticle

    TY - JOUR

    T1 - Automated Extraction of Product Comparison Matrices From Informal Product Descriptions

    AU - Ben Nasr, Sana

    AU - Bécan, Guillaume

    AU - Acher, Mathieu

    AU - Filho, João Bosco Ferreira

    AU - Sannier, Nicolas

    AU - Baudry, Benoit

    AU - Davril, Jean-Marc

    PY - 2017/2/1

    Y1 - 2017/2/1

    N2 - Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product. In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users.

    AB - Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product. In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users.

    KW - Feature mining

    KW - Product comparison matrices

    KW - Reverse engineering

    KW - Software product lines

    KW - Variability mining

    UR - http://www.scopus.com/inward/record.url?scp=84996593342&partnerID=8YFLogxK

    U2 - 10.1016/j.jss.2016.11.018

    DO - 10.1016/j.jss.2016.11.018

    M3 - Article

    VL - 124

    SP - 82

    EP - 103

    JO - Journal of Systems and Software

    T2 - Journal of Systems and Software

    JF - Journal of Systems and Software

    SN - 0164-1212

    ER -