Statistical Model Checking for Product Lines

International audienceWe report on the suitability of statistical model checking forthe analysis of quantitative properties of product line models by an extendedtreatment of earlier work by the authors. The type of analysis thatcan be performed includes the likelihood of specific product behaviour,the expected average cost of products (in terms of the attributes of theproducts’ features) and the probability of features to be (un)installed atruntime. The product lines must be modelled in QFLan, which extendsthe probabilistic feature-oriented language PFLan with novel quantitativeconstraints among features and on behaviour and with advancedfeature installation options. QFLan is a rich process-algebraic specifi-cation language whose operational behaviour interacts with a store ofconstraints, neatly separating product configuration from product behaviour.The resulting probabilistic configurations and probabilistic behaviourconverge in a discrete-time Markov chain semantics, enablingthe analysis of quantitative properties. Technically, a Maude implementationof QFLan, integrated with Microsoft’s SMT constraint solver Z3,is combined with the distributed statistical model checker MultiVeStA,developed by one of the authors. We illustrate the feasibility of our frameworkby applying it to a case study of a product line of bikes

Family-Based Model Checking with mCRL2

\u3cp\u3eFamily-based model checking targets the simultaneous verfication of multiple system variants, a technique to handle feature-based variability that is intrinsic to software product lines (SPLs). We present an approach for family-based verification based on the feature μ-calculus μL\u3csub\u3ef\u3c/sub\u3e, which combines modalities with feature expressions. This logic is interpreted over featured transition systems, a well-accepted model of SPLs, which allows one to reason over the collective behavior of a number of variants (a family of products). Via an embedding into the modal μ-calculus with data, underpinned by the general-purpose mCRL2 toolset, off-the-shelf tool support for μLf becomes readily available. We illustrate the feasibility of our approach on an SPL benchmark model and show the runtime improvement that family-based model checking with mCRL2 offers with respect to model checking the benchmark product-by-product.\u3c/p\u3

QFLan: A tool for the quantitative analysis of highly reconfigurable systems

QFLan offers modeling and analysis of highly reconfigurable systems, like product lines, which are characterized by combinatorially many system variants (or products) that can be obtained via different combinations of installed features. The tool offers a modern integrated development environment for the homonym probabilistic feature-oriented language. QFLan allows the specification of a family of products in terms of a feature model with quantitative attributes, which defines the valid feature combinations, and probabilistic behavior subject to quantitative constraints. The language’s behavioral part enables dynamic installation, removal and replacement of features. QFLan has a discrete-time Markov chain semantics, permitting quantitative analyses. Thanks to a seamless integration with the statistical model checker MultiVeStA, it allows for analyses like the likelihood of specific behavior or the expected average value of non-functional aspects related to feature attributes

Structured Synthesis for Probabilistic Systems

Contains fulltext : 204626.pdf (preprint version ) (Closed access) Contains fulltext : 204626pub.pdf (publisher's version ) (Closed access)NASA Formal Methods: 11th International Symposium, NFM 2019, Houston, TX, USA, May 7–9, 201