Feature Model Extraction from Large Collections of Informal Product Descriptions

Student thesis: Master typesMaster in Computer science

Abstract

Feature Models (FMs) have become one of the most popular form of representation to model the commonalities and the variabilities of products in Software Product Lines (SPL) engineering. The task of manually creating an FM can be tedious and error-prone, which is why researchers have developed techniques for automatically extracting FMs from formally defined product descriptions (artifacts). The problem is that those artifacts are often not available, which is why we present a novel, fully automated approach for extracting FMs from publicly available product descriptions that can be found on software repository 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 a fairly comprehensive coverage. Our approach utilizes hundreds of product descriptions to construct an FM and is described and evaluated against antivirus product descriptions mined from SoftPedia.
Date of Award3 Sep 2013
Original languageEnglish
Awarding Institution
  • University of Namur
SupervisorPatrick Heymans (Supervisor)

Cite this

Feature Model Extraction from Large Collections of Informal Product Descriptions
Davril, J. (Author), Delfosse, E. (Author). 3 Sep 2013

Student thesis: Master typesMaster in Computer science