Axel Legay

Rafael Olaechea, Uli Fahrenberg, Joanne M. Atlee et al.

A software product line is a family of software products that share a common set of mandatory features and whose individual products are differentiated by their variable (optional or alternative) features. Family-based analysis of software product lines takes as input a single model of a complete product line and analyzes all its products at the same time. As the number of products in a software product line may be large, this is generally preferable to analyzing each product on its own. Family-based analysis, however, requires that standard algorithms be adapted to accomodate variability. In this paper we adapt the standard algorithm for computing limit average cost of a weighted transition system to software product lines. Limit average is a useful and popular measure for the long-term average behavior of a quality attribute such as performance or energy consumption, but has hitherto not been available for family-based analysis of software product lines. Our algorithm operates on weighted featured transition systems, at a symbolic level, and computes limit average cost for all products in a software product line at the same time. We have implemented the algorithm and evaluated it on several examples.

Maxime Cordy, Patrick Heymans, Pierre-Yves Schobbens et al.

Many product lines are critical, and therefore reliability is a vital part of their requirements. Reliability is a probabilistic property. We therefore propose a model for feature-aware discrete-time Markov chains as a basis for verifying probabilistic properties of product lines, including reliability. We compare three verification techniques: The enumerative technique uses PRISM, a state-of-the-art symbolic probabilistic model checker, on each product. The parametric technique exploits our recent advances in parametric model checking. Finally, we propose a new bounded technique that performs a single bounded verification for the whole product line, and thus takes advantage of the common behaviours of the product line. Experimental results confirm the advantages of the last two techniques.

Xavier Devroey, Maxime Cordy, Gilles Perrouin et al.

Software Product Lines (SPL) are inherently difficult to test due to the combinatorial explosion of the number of products to consider. To reduce the number of products to test, sampling techniques such as combinatorial interaction testing have been proposed. They usually start from a feature model and apply a coverage criterion (e.g. pairwise feature interaction or dissimilarity) to generate tractable, fault-finding, lists of configurations to be tested. Prioritization can also be used to sort/generate such lists, optimizing coverage criteria or weights assigned to features. However, current sampling/prioritization techniques barely take product behavior into account. We explore how ideas of statistical testing, based on a usage model (a Markov chain), can be used to extract configurations of interest according to the likelihood of their executions. These executions are gathered in featured transition systems, compact representation of SPL behavior. We discuss possible scenarios and give a prioritization procedure illustrated on an example.