We study models of software systems with variants that stem from a specific choice of configuration parameters with a direct impact on performance properties. Using UML activity diagrams with quantitative annotations, we model such systems as a product line. The efficiency of a product-based evaluation is typically low because each product must be analyzed in isolation, making difficult the re-use of computations across variants. Here, we propose a family-based approach based on symbolic computation. A numerical assessment on large activity diagrams shows that this approach can be up to three orders of magnitude faster than product-based analysis in large models, thus enabling computationally efficient explorations of large parameter spaces.
Manufacturing systems exist in many different variants and evolve over time in order to meet changing requirements or environment contexts. This leads to an increased design complexity as well as to increased maintenance effort. In order to appropriately handle this inherent complexity, we propose a multi-perspective modeling approach combining UML activity, component-based and state chart diagrams to separately represent different system aspects. We combine the multi-perspective modeling approach with delta modeling to capture the variability and evolution of these manufacturing systems. Delta modeling allows a flexible, yet concise and understandable representation of variability in a modular manner. We examine our approach by applying it to a manufacturing lab demonstrator system with automated code generation from models obtained by delta application.
Abstract-In software performance engineering, what-if scenarios, architecture optimization, capacity planning, run-time adaptation, and uncertainty management of realistic models typically require the evaluation of many instances. Effective analysis is however hindered by two orthogonal sources of complexity. The first is the infamous problem of state space explosion-the analysis of a single model becomes intractable with its size. The second is due to massive parameter spaces to be explored, but such that computations cannot be reused across model instances. In this paper, we efficiently analyze many queuing models with the distinctive feature of more accurately capturing variability and uncertainty of execution rates by incorporating general (i.e., non-exponential) distributions. Applying product-line engineering methods, we consider a family of models generated by a core that evolves into concrete instances by applying simple delta operations affecting both the topology and the model's parameters. State explosion is tackled by turning to a scalable approximation based on ordinary differential equations. The entire model space is analyzed in a family-based fashion, i.e., at once using an efficient symbolic solution of a super-model that subsumes every concrete instance. Extensive numerical tests show that this is orders of magnitude faster than a naive instance-by-instance analysis.
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