Abstract
Feature models (FMs) are widely used to describe software product lines (SPL). They specify common and variation points in the domain of the SPL. Because of their wide domain coverage, FM development requires extensive domain analysis and important domain expertise that is often hard to acquire. These development requirements motivated us to propose, in this paper, a top-down method that extracts automatically an FM from functional requirements of product variants. Besides its automation, its novelty stems from the use of semantic information mined through natural language processing techniques to extract potential features from each product variant. To account for name variations, our method harmonizes the names of the features extracted from the product variants by using a classification technique to group similar features. In addition, to determine the feature type, it uses the formal concept analysis technique to distinguish mandatory from optional features. Furthermore, to structure the FM, it uses a set of semantic criteria to determine the constraints among the features. The paper reports on a quantitative and a comparative evaluation of the method on existing FMs, and it examines the conformity of the generated FMs to the input functional requirements, based on experts' feedbacks.