A framework for quantitative modeling and analysis of highly (re)configurable systems
Maurice H. ter Beek, Axel Legay, Alberto Lluch Lafuente, Andrea, Vandin

TL;DR
This paper introduces a probabilistic modeling framework using QFLAN for highly configurable systems, enabling scalable quantitative analysis of system variants through statistical model checking and a dedicated Eclipse tool.
Contribution
It presents a novel probabilistic feature-oriented language and analysis approach for scalable quantitative analysis of highly configurable systems.
Findings
Effective modeling of probabilistic behavior in configurable systems
Scalable analysis using statistical model checking techniques
Validated framework with multiple case studies
Abstract
This paper presents our approach to the quantitative modeling and analysis of highly (re)configurable systems, such as software product lines. Different combinations of the optional features of such a system give rise to combinatorially many individual system variants. We use a formal modeling language that allows us to model systems with probabilistic behavior, possibly subject to quantitative feature constraints, and able to dynamically install, remove or replace features. More precisely, our models are defined in the probabilistic feature-oriented language QFLAN, a rich domain specific language (DSL) for systems with variability defined in terms of features. QFLAN specifications are automatically encoded in terms of a process algebra whose operational behavior interacts with a store of constraints, and hence allows to separate system configuration from system behavior. The resulting…
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Taxonomy
TopicsAdvanced Software Engineering Methodologies · Model-Driven Software Engineering Techniques · Business Process Modeling and Analysis
