What do you give for free to your competitor when you exhibit a product line? This paper addresses this question through several cases in which the discovery of trade secrets of a product line is possible and can lead to severe consequences. That is, we show that an outsider can understand the variability realization and gain either confidential business information or even some economical direct advantage. For instance, an attacker can identify hidden constraints and bypass the product line to get access to features or copyrighted data. This paper warns against possible naive modeling, implementation, and testing of variability leading to the existence of product lines that jeopardize their trade secrets. Our vision is that defensive methods and techniques should be developed to protect specifically variability – or at least further complicate the task of reverse engineering it.

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.

Many real-world product lines are only represented as nonhierarchical collections of distinct products, described by their configuration values. As the manual preparation of feature models is a tedious and labour-intensive activity, some techniques have been proposed to automatically generate boolean feature models from product descriptions. However, none of these techniques is capable of synthesizing feature attributes and relations among attributes, despite the huge relevance of attributes for documenting software product lines. In this paper, we introduce for the first time an algorithmic and parametrizable approach for computing a legal and appropriate hierarchy of features, including feature groups, typed feature attributes, domain values and relations among these attributes. We have performed an empirical evaluation by using both randomized configuration matrices and real-world examples. The initial results of our evaluation show that our approach can scale up to matrices containing 2,000 attributed features, and 200,000 distinct configurations in a couple of minutes.

Product Comparison Matrices (PCMs) form a rich source of data for comparing a set of related and competing products over numerous features. Despite their apparent simplicity, PCMs contain heterogeneous, ambiguous, uncontrolled and partial information that hinders their efficient exploitations. In this paper, we formalize PCMs through model-based automated techniques and develop additional tooling to support the edition and re-engineering of PCMs. 20 participants used our editor to evaluate the PCM metamodel and automated transformations. The results over 75 PCMs from Wikipedia show that (1) a significant proportion of the formalization of PCMs can be automated -- 93.11% of the 30061 cells are correctly formalized; (2) the rest of the formalization can be realized by using the editor and mapping cells to existing concepts of the metamodel. The automated approach opens avenues for engaging a community in the mining, re-engineering, edition, and exploitation of PCMs that now abound on the Internet.