Poster: On Extracting Relevant and Complex Variability Information from Software Descriptions with Pattern Structures

The migration from existing software variants to a software product line is an arduous task that necessitates to synthesise a variability model based on already developed softwares. Nowadays, the increasing complexity of software product lines compels practitioners to design more complex variability models that represent other information than binary features, e.g., multi-valued attributes. Assisting the extraction of complex variability models from variant descriptions is a key task to help the migration towards complex software product lines. In this paper, we address the problem of extracting complex variability information from software descriptions, as a part of the process of complex variability model synthesis. We propose an approach based on Pattern Structures to extract variability information, in the form of logical relationships involving both binary features and multi-valued attributes.