Abstract:Abstract. Automatic prediction tools play a key role in enabling the application of non-functional requirements analysis to selection and assembly of components for Component-Based Systems, reducing the need for strong mathematical skills to software designers. Exploiting the paradigm of Model Driven Engineering (MDE), it is possible to automate transformations from design models to analytical models, enabling for formal property verification. MDE is the core paradigm of KlaperSuite presented in this paper, wh… Show more
“…This paper specifically focuses on the evaluation of the reliability prediction capabilities of KlaperSuite, hence we give an extensive overview of how these analyses work in Section 4, while we defer to Appendix A for details about the model transformations that allow to carry out the analysis. For what concerns performance, KLAPER already provides both an LQN based prediction tool and the SimJava 3 -based simulator (a description of these tools can be found in [14,60]). We are currently in the stage of refactoring and fully integrating these analyzers into KlaperSuite; their usability is currently limited.…”
Section: The Klapersuite Frameworkmentioning
confidence: 99%
“…In this article we present KlaperSuite, our model-driven proposal to support early-stage analysis of non-functional attributes for component-based systems [68] which we already introduced in [14].…”
Automatic prediction tools play a key role in enabling the application of non-functional requirements analysis, to simplify the selection and the assembly of components for component-based software systems, and in reducing the need for strong mathematical skills for software designers. By exploiting the paradigm of Model-Driven Engineering (MDE), it is possible to automatically transform design models into analytical models, thus enabling formal property verification. MDE is the core paradigm of the KlaperSuite framework presented in this paper, which exploits the KLAPER pivot language to fill the gap between design and analysis of component-based systems for reliability properties. KlaperSuite is a family of tools empowering designers with the ability to capture and analyze QoS views of their systems, by building a one-click bridge towards a number of established verification instruments. In this article we concentrate on the reliability prediction capabilities of KlaperSuite and we evaluate them with respect to several case studies from literature and industry.
“…This paper specifically focuses on the evaluation of the reliability prediction capabilities of KlaperSuite, hence we give an extensive overview of how these analyses work in Section 4, while we defer to Appendix A for details about the model transformations that allow to carry out the analysis. For what concerns performance, KLAPER already provides both an LQN based prediction tool and the SimJava 3 -based simulator (a description of these tools can be found in [14,60]). We are currently in the stage of refactoring and fully integrating these analyzers into KlaperSuite; their usability is currently limited.…”
Section: The Klapersuite Frameworkmentioning
confidence: 99%
“…In this article we present KlaperSuite, our model-driven proposal to support early-stage analysis of non-functional attributes for component-based systems [68] which we already introduced in [14].…”
Automatic prediction tools play a key role in enabling the application of non-functional requirements analysis, to simplify the selection and the assembly of components for component-based software systems, and in reducing the need for strong mathematical skills for software designers. By exploiting the paradigm of Model-Driven Engineering (MDE), it is possible to automatically transform design models into analytical models, thus enabling formal property verification. MDE is the core paradigm of the KlaperSuite framework presented in this paper, which exploits the KLAPER pivot language to fill the gap between design and analysis of component-based systems for reliability properties. KlaperSuite is a family of tools empowering designers with the ability to capture and analyze QoS views of their systems, by building a one-click bridge towards a number of established verification instruments. In this article we concentrate on the reliability prediction capabilities of KlaperSuite and we evaluate them with respect to several case studies from literature and industry.
“…KLAPER [7] is a meta model language for performance prediction of component based systems. With the KLAPER Suite, there is a set of tools to actually create performance prediction from the model.…”
Complex information flows in the domain of industrial software systems complicate the creation of performance models to validate the challenging performance requirements. Performance models using annotated UML diagrams or mathematical notations are difficult to discuss with stakeholders from the industrial automation domain, who often have a limited software engineering background. We introduce a novel model transformation from Use Case Maps (UCM) to the Palladio Component Model (PCM), which enables performance modeling based on an intuitive notation for complex information flows. The resulting models can be solved using existing simulators or analytical solvers. We validated the correctness of the transformation with three case study models, and performed a user study. The results showed a performance prediction deviation of less than 10 percent compared to a reference model in most cases.
“…Precisely, by expanding first the c rows representing the variable states (each has τ symbolic terms), we need to compute at most τ c determinants and then linearly combine them. Each submatrix of size t − c does not contain any variable symbol, by construction, thus its determinant can be computed with (t − c) 3 operations among constant numbers (LU-decomposition), thus much faster than the corresponding ones among polynomials. The final complexity is thus τ c · (t − c) 3 ∼ τ c · t 3 , which significantly reduces the original complexity and makes the design-time pre-computation of reachability properties feasible in a reasonable time, even for large values of t.…”
Section: Current Achievementsmentioning
confidence: 99%
“…This research has been partially funded by the European Commission, Programme IDEAS-ERC, Project 227977-SMScom. Other publications of the author include [10,4,7,3,12,13,11]. He is also co-author of the Dagstuhl proceedings book "Model Driven Quality Prediction" (Springer ed.…”
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