MARTE Mechanisms to Model Variability When Analyzing Embedded Software Product Lines

Belategi, Lorea; Sagardui, Goiuria; Etxeberria, Leire

doi:10.1007/978-3-642-15579-6_38

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…Some of the works are concerned with the interactions between selected features and the NFRs and propose different techniques to represent these interactions and dependencies. In [8], the MARTE profile is analyzed to identify the variability mechanisms of the profile in order to model variability in embedded SPL models. Although MARTE was not defined for product lines, the paper proposes to combine it with existing mechanisms for representing variability, but it does not explain how this can be achieved.…”

Section: Performance Resultsmentioning

confidence: 99%

Software Performance Modeling

Petriu

Alhaj

Tawhid

2012

Formal Methods for Model-Driven Engineering

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Abstract. Ideally, a software development methodology should include both the ability to specify non-functional requirements and to analyze them starting early in the lifecycle; the goal is to verify whether the system under development would be able to meet such requirements. This chapter considers quantitative performance analysis of UML software models annotated with performance attributes according to the standard "UML Profile for Modeling and Analysis of Real-Time and Embedded Systems" (MARTE). The chapter describes a model transformation chain named PUMA (Performance by Unified Model Analysis) that enables the integration of performance analysis in a UML-based software development process, by automating the derivation of performance models from UML+MARTE software models, and by facilitating the interoperability of UML tools and performance tools. PUMA uses an intermediate model called "Core Scenario Model" (CSM) to bridge the gap between different kinds of software models accepted as input and different kinds of performance models generated as output. Transformation principles are described for transforming two kinds of UML behaviour representation (sequence and activity diagrams) into two kinds of performance models (Layered Queueing Networks and stochastic Petri nets). Next, PUMA extensions are described for two classes of software systems: service-oriented architecture (SOA) and software product lines (SPL). IntroductionThe quality of many software intensive systems, ranging from real-time embedded systems to web-based applications, is determined to a large extent by their performance characteristics, such as response time and throughput. The developers of such systems should be able to assess and understand the performance effects of various design decisions starting at an early stage and continuing throughout the software life cycle. Software Performance Engineering (SPE) is an approach introduced by Smith [37], which proposes to use quantitative methods and performance models in order to assess the performance effects of different design and implementation alternatives during the development of a system. SPE promotes the integration of performance analysis into the software development process from its earliest lifecycle stages, in order to insure that the system will meet its performance objectives.

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Section: Performance Resultsmentioning

confidence: 99%

Software Performance Modeling

Petriu

Alhaj

Tawhid

2012

Formal Methods for Model-Driven Engineering

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“…[73] SimPL methodology provides a notation and a set of guidelines for modelling commonalities and variabilities in ICS families. [74] This paper analyses the MARTE profile and its capability to model variability. [75] 4SRS method aimed at SPLs, addressing the transformation of functional requirements into component-based requirements for SPLs.…”

Section: Appendix B Selected Papers -Final Listmentioning

confidence: 99%

“…[85] A formal syntax and semantics to document variability, providing the foundation for an automated support of QA in domain engineering. [54] Micro and macro workflow variability for designing variability in the component design and software design phase, based on components.…”

Section: Appendix B Selected Papers -Final Listmentioning

confidence: 99%

Requirements modeling languages for software product lines: A systematic literature review

Sepúlveda

Cravero

Luján-Mora

2016

Information and Software Technology

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“…The MARTE profile is analyzed in [5], to identify the variability mechanisms of the profile in order to model variability in embedded SPL models. Although MARTE was defined for analyzing single systems, the paper proposes to combine it with existing mechanisms for representing variability modeling and management for SPL.…”

Section: Related Workmentioning

confidence: 99%

Automatic Derivation of a Product Performance Model from a Software Product Line Model

Tawhid

Petriu

2011

2011 15th International Software Product Line Conference

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We propose to integrate performance analysis in the early phases of the model-driven development process for Software Product Lines (SPL). We start with a multi-view UML model of the core family assets representing the commonality and variability between different products, which we call the SPL model. We add another perspective to the SPL model, annotating it with generic performance specifications expressed in the standard UML profile MARTE, recently adopted by OMG. The runtime performance of a product is affected by factors contained in the UML model of the product (derived from the SPL model), but also by external factors depending on the implementation and execution environments. The external factors not contained in the SPL model need to be eventually represented in the performance model. In order to do so, we propose to represent the variability space of different possible implementation and execution environments through a so called "performance completion (PC) feature model". These PC features are mapped to MARTE performancerelated stereotypes and attributes attached to the SPL model elements. A first model transformation realized in the Atlas Transformation Language (ATL) derives the UML model of a specific product with concrete MARTE annotations from the SPL model. A second transformation generates a Layered Queueing Network (LQN) performance model for the given product by applying an existing transformation named PUMA, developed in previous work. The proposed technique is illustrated with an e-commerce case study. A LQN model is derived for a product and the impact of different levels of secure communication channels on its performance is analyzed by using the LQN model.

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MARTE Mechanisms to Model Variability When Analyzing Embedded Software Product Lines

Cited by 11 publications

References 7 publications

Software Performance Modeling

Software Performance Modeling

Requirements modeling languages for software product lines: A systematic literature review

Automatic Derivation of a Product Performance Model from a Software Product Line Model

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