Feature Model Extraction from Large Collections of Informal Product Descriptions

Jean-Marc Davril 1 Edouard Delfosse 1 Negar Hariri 2 Mathieu Acher 3 Jane Clelang-Huang 2 Patrick Heymans 4
3 TRISKELL - Reliable and efficient component based software engineering
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
4 PReCISE
PReCISE - PReCISE Research Centre in Information Systems Engineering
Abstract : Feature Models (FMs) are used extensively in software product line engineering to help generate and validate individual product configurations and to provide support for domain analysis. As FM construction can be tedious and time-consuming, researchers have previously developed techniques for extracting FMs from sets of formally specified individual configurations, or from software requirements specifications for families of existing products. However, such artifacts are often not available. In this paper we present a novel, automated approach for constructing FMs from publicly available product descriptions found in online product repositories and marketing websites such as SoftPedia and CNET. While each individual product description provides only a partial view of features in the domain, a large set of descriptions can provide fairly comprehensive coverage. Our approach utilizes hundreds of partial product descriptions to construct an FM and is described and evaluated against antivirus product descriptions mined from SoftPedia.
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Jean-Marc Davril, Edouard Delfosse, Negar Hariri, Mathieu Acher, Jane Clelang-Huang, et al.. Feature Model Extraction from Large Collections of Informal Product Descriptions. European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE'13), Sep 2013, Saint Petersburg, Russia. pp.290-300, ⟨10.1145/2491411.2491455⟩. ⟨hal-00859475⟩

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