Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking
This work addresses the need for quantitative analysis in software product lines with probabilistic configurations, though it appears incremental as it builds on existing languages and tools.
The authors tackled the problem of analyzing quantitative properties of software product line models with probabilistic aspects by enriching the feature-oriented language FLan with action rates to create PFLan, and they demonstrated its application through a case study using statistical model checking with MultiVeStA, resulting in analyses such as the likelihood of product malfunctioning and expected average cost.
We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLan with action rates, which specify the likelihood of exhibiting particular behaviour or of installing features at a specific moment or in a specific order. The enriched language (called PFLan) allows us to specify models of software product lines with probabilistic configurations and behaviour, e.g. by considering a PFLan semantics based on discrete-time Markov chains. The Maude implementation of PFLan is combined with the distributed statistical model checker MultiVeStA to perform quantitative analyses of a simple product line case study. The presented analyses include the likelihood of certain behaviour of interest (e.g. product malfunctioning) and the expected average cost of products.