Title of paper: A Framework for Quantitative Modeling and Analysis of Highly (re)configurable Systems

Abstract

This short paper summarises the contributions published in (ter Beek et al., 2018), where we introduce QFLan, a framework for quantitative modeling and analysis of highly (re)configurable systems, like software product lines. We define a rich domain specific language (DSL) for systems with variability in terms of features, which can be dynamically installed, removed or replaced, capable of modeling probabilistic behavior, possibly subject to quantitative feature constraints. High-level DSL specifications are automatically encoded in a process algebra whose operational behavior interacts with a store of constraints, which allows to separate a system's configuration from its behavior. The resulting probabilistic configurations and behavior converge seamlessly in a semantics based on discrete-time Markov chains, thus enabling quantitative analysis. An accompanying Eclipse-based tool offers a modern integrated development environment to specify such systems and to perform analyses that range from the likelihood of specific behavior to the expected average cost, in terms of feature attributes, of specific system variants. Based on a seamless integration with the statistical model checker MultiVeStA, QFLan allows to scale to larger models with respect to precise probabilistic analysis techniques. We provide a number of case studies that have driven and validated the development of the QFLan framework. In particular, we show the versatility of the QFLan framework with an application to risk analysis of a safe lock system from the security domain.