Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking

Maurice H. ter Beek
(ISTI-CNR, Pisa, Italy)
Axel Legay
(Inria, Rennes, France)
Alberto Lluch Lafuente
(DTU, Lyngby, Denmark)
Andrea Vandin
(University of Southampton, UK)

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.

In Joanne M. Atlee and Stefania Gnesi: Proceedings 6th Workshop on Formal Methods and Analysis in SPL Engineering (FMSPLE 2015), London, UK, 11 April 2015, Electronic Proceedings in Theoretical Computer Science 182, pp. 56–70.
Published: 12th April 2015.

ArXived at: http://dx.doi.org/10.4204/EPTCS.182.5 bibtex PDF
References in reconstructed bibtex, XML and HTML format (approximated).
Comments and questions to: eptcs@eptcs.org
For website issues: webmaster@eptcs.org