The Tropos Software Engineering Methodology

Morandini, Mirko; Dalpiaz, Fabiano; Nguyen, Cu; Siena, Alberto

doi:10.1007/978-3-642-39975-6_14

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…Among the MAS methodologies, Prometheus [8], ADELFE [17], OSOAD [18], TROPOS [22], and MASSIVE [21] apply the UML UCD without specifying agents or its internal behaviors.…”

Section: Related Workmentioning

confidence: 99%

MASRML - A Domain-specific Modeling Language for Multi-agent Systems Requirements

Guedes¹,

Filho²,

Gaedicke³

et al. 2020

IJSEA

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MASRML-Multi-Agent Systems Requirements Modeling Languageis a UML-based Domain-Specific Modeling Language conceived for the requirements modeling in multi-agent system projects. Along this work the extended metamodel developed to support the language is described and the applicability of this DSML in the requirements identification of a multi-agent system is demonstrated using the new mechanisms produced to model specific functional requirements for this kind of system. This work also includes how the DSML was validated and the impressions collected during the validations.

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“…Among the MAS methodologies, Prometheus [8], ADELFE [17], OSOAD [18], TROPOS [22], and MASSIVE [21] apply the UML UCD without specifying agents or its internal behaviors.…”

Section: Related Workmentioning

confidence: 99%

MASRML - A Domain-specific Modeling Language for Multi-agent Systems Requirements

Guedes¹,

Filho²,

Gaedicke³

et al. 2020

IJSEA

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“…Tropos uses a modeling framework based on i* [62] which proposes the concepts of actor, goal, plan, resource and social dependency to model both the system-to-be and its organizational operating environment [15] [44].…”

Section: The Tropos Key Conceptsmentioning

confidence: 99%

Autonomic goal-driven deployment in heterogeneous computing environments

Rodrigues¹

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We see a growing interest in computing applications that should rely on heterogeneous computing environments, like Internet of Things (IoT). Such applications are intended to execute in a broad range of devices with different available computing resources. In order to handle some kind of heterogeneity, such as two possible types of graphical processors in a desktop computer, we can use simple approaches as a script at deployment-time that chooses the right software library to be copied to a folder. These simple approaches are centralized and created at design-time. They require one specialist or team to control the entire space of variability. However, such approaches are not scalable to highly heterogeneous environments. In highly dynamic and heterogeneous environment it is hard to predict the computing environment at design-time, implying likely undecidability on the correct configuration for each environment at design-time. In our work, we propose GoalD: a method that allows autonomous deployment of systems by reflecting about the goals of the system and its computing environment. By autonomous deployment, we mean that the system can find the correct set of components, for the target computing environment, without human intervention.We evaluate our approach on the filling station advisor case study where an application advises a driver where to refuel/recharge its vehicle. We design the application with variability at requirements, architecture, and deployment, which can allow the designed application be executed in different devices. For scenarios with different environments, it was possible to plan the deployment autonomously. Additionally, the scalability of the algorithm that plan the deployment was evaluated in a simulated environment. Results show that using the approach it is possible to autonomously plan the deployment of a system with thousands of components in few seconds.

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