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

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.

[1]  Felice Dell'Orletta,et al.  Mining commonalities and variabilities from natural language documents , 2013, SPLC '13.

[2]  Rubén Prieto-Díaz,et al.  DARE: Domain analysis and reuse environment , 1998, Ann. Softw. Eng..

[3]  Felice Dell'Orletta,et al.  T2K^2: a System for Automatically Extracting and Organizing Knowledge from Texts , 2014, LREC.

[4]  Alessio Ferrari,et al.  Product Line Engineering Applied to CBTC Systems Development , 2012, ISoLA.

[5]  Haiyan Zhao,et al.  An approach to constructing feature models based on requirements clustering , 2005, 13th IEEE International Conference on Requirements Engineering (RE'05).

[6]  Songbo Tan,et al.  Neighbor-weighted K-nearest neighbor for unbalanced text corpus , 2005, Expert Syst. Appl..

[7]  Christoph Pohl,et al.  An Exploratory Study of Information Retrieval Techniques in Domain Analysis , 2008, 2008 12th International Software Product Line Conference.

[8]  Simonetta Montemagni,et al.  A Contrastive Approach to Multi-word Extraction from Domain-specific Corpora , 2010, LREC.

[9]  Zarinah Mohd Kasirun,et al.  Feature extraction approaches from natural language requirements for reuse in software product lines: A systematic literature review , 2015, J. Syst. Softw..

[10]  Jane Cleland-Huang,et al.  On-demand feature recommendations derived from mining public product descriptions , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[11]  Nan Niu,et al.  Extracting and Modeling Product Line Functional Requirements , 2008, 2008 16th IEEE International Requirements Engineering Conference.

[12]  Isabel John,et al.  Capturing Product Line Information from Legacy User Documentation , 2006, Software Product Lines.

[13]  Kyo Chul Kang,et al.  Feature-Oriented Domain Analysis (FODA) Feasibility Study , 1990 .

[14]  Nan Niu,et al.  On-Demand Cluster Analysis for Product Line Functional Requirements , 2008, 2008 12th International Software Product Line Conference.

[15]  Mathieu Acher,et al.  On extracting feature models from product descriptions , 2012, VaMoS.

[16]  Roos Frantz,et al.  Automated Analysis of Software Product Lines with Orthogonal Variability Models: Extending the Fama Ecosystem. , 2013 .

[17]  Don S. Batory,et al.  Feature Models, Grammars, and Propositional Formulas , 2005, SPLC.

[18]  Ruzanna Chitchyan,et al.  A framework for constructing semantically composable feature models from natural language requirements , 2009, SPLC.

[19]  Donald D. Cowan,et al.  S.P.L.O.T.: software product lines online tools , 2009, OOPSLA Companion.

[20]  Felice Dell'Orletta,et al.  Ensemble system for Part-of-Speech tagging , 2009 .

[21]  Mathieu Acher,et al.  Feature model extraction from large collections of informal product descriptions , 2013, ESEC/FSE 2013.