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

Alessio Ferrari, G. Spagnolo, S. Gnesi, F. Dell’Orletta
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引用次数: 9

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.
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CMT和FDE:在自然语言文档和特征图之间架起桥梁的工具
一个希望进入一个成熟的技术市场的商业主体需要准确地分析不同竞争对手的产品特征。这些特性通常可以通过自然语言(NL)小册子或NL网页访问,这些手册或网页向潜在客户描述产品。建立一个层次化地总结竞争产品中可用的不同特征的特征模型,可以为市场分析带来相应的好处。公司可以很容易地可视化现有功能,并推断出可用解决方案未涵盖的方面。然而,从现有产品的公开可用文档开始设计特征模型是一项耗时且容易出错的任务。本文提出了共性挖掘工具(common Mining Tool, CMT)和特征图编辑器(Feature Diagram Editor, FDE)两种工具,它们可以共同支持特征模型定义过程。通过利用基于对比分析的自然语言处理(NLP)方法,CMT允许从现有产品的NL描述中挖掘公共和变体特征,该方法允许从NL文档中识别领域相关术语。FDE将CMT提取的共性和差异性以可视化的形式呈现出来。此外,FDE还允许通过直观的GUI对最终特征模型进行图形化设计和细化。
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