Abstract
Software systems are now expected to operate in uncertain and dynamic contexts with constantly changing operational conditions. In order to maintain the requisite Quality of Service (QoS), self-adaptation has been proposed as a realistic and effective way to deal with the rising complexity, unpredictability, and dynamics of these systems. Therefore, self-adaptive systems (SASs) engineering requires innovative modeling and development approaches as model-driven development concepts. Moreover, designing SAS using formal methods is very beneficial since it provides a foundation for ensuring that the specification and implementation are consistent. They represent a promising and effective method for rigorously specifying mathematical models of software systems and analyzing their behavior. However, adopting and applying formal models is a hard task because they require great expertise and face some reticence from designers. An easiest and more comprehensible domain-specific language (DSL) will contribute considerably to remediate these difficulties by automatically generating the underlying formal specifications. This paper presents a DSL for SASs modelling, called Aster. The language is used to define the architectural pieces of a SAS and their interrelationships. It mainly offers the necessary concepts to define the SLA to be maintained by a SAS and additional artifacts to manage the intended qualities. Aster also includes principles for defining SAS’s behavioral components, such as identifying and applying adaptive actions. Furthermore, the precise definition of the modeling concepts via corresponding meta-models enables the system specification to be correct by construction.
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Kachi, F., Bouanaka, C. (2022). Aster: A DSL for Engineering Self-Adaptive Systems. In: Senouci, M.R., Boulahia, S.Y., Benatia, M.A. (eds) Advances in Computing Systems and Applications. CSA 2022. Lecture Notes in Networks and Systems, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-031-12097-8_4
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DOI: https://doi.org/10.1007/978-3-031-12097-8_4
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