Towards Statistical Prioritization for Software Product Lines Testing
This work addresses testing challenges for software engineers dealing with combinatorial explosion in SPL, offering an incremental approach by incorporating product behavior into prioritization.
The paper tackles the problem of testing Software Product Lines (SPL) by proposing a statistical prioritization method based on usage models and featured transition systems to select configurations based on execution likelihood, aiming to improve fault detection efficiency.
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.