Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condensed view of a product line. This paper introduces MatrixMiner: a tool for automatically synthesizing product comparison matrices (PCMs) from a set of product descriptions written in natural language. MatrixMiner is capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. Our empirical results of products mined from BestBuy show that the synthesized PCMs exhibit numerous quantitative, comparable information. Users can exploit MatrixMiner to visualize the matrix through a Web editor and review, refine, or complement the cell values thanks to the traceability with the original product descriptions and technical specifications.

Software-intensive systems often consist of multiple components that interact to realize complex requirements. An additional dimension of complexity arises when one designs many variants of a system at once, that is, a software product line (SPL). We propose a scenario-based approach to design SPLs, based on a combination of Modal Sequence Diagrams (MSDs) and a feature model. It consists in associating every MSD to the set of variants that have to satisfy its specification. Variability constitutes a new source of complexity, which can lead to inconsistencies in the specification of one or multiple variants. It is therefore crucial to detect these inconsistencies, and to produce a controller for each variant that makes it behave so that it satisfies its specification. We present a new controller synthesis technique that checks the absence of inconsistencies in all variants at once, thereby more radically exploiting the similarities between them. Our method first translates the MSD specification into a variability-aware Büchi game, and then solves this game for all variants in a single execution. We implemented the approach in ScenarioTools, a software tool which we use to evaluate our algorithms against competing methods.

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.