Abstract
Performance problems such as sluggish response time or low throughput are especially annoying, frustrating and noticeable to users. Fixing performance problems after they occur results in unplanned expenses and time. Our vision is an MDE-intensive software development paradigm for complex systems in which software designers can evaluate performance early in development, when the analysis can have the greatest impact. We seek to empower designers to do the analysis themselves by automating the creation of performance models out of standard design models. Such performance models can be automatically solved, providing results meaningful to them. In our vision, this automation can be enabled by using model-to-model transformations: First, designers create UML design models embellished with the Modeling and Analysis of Real Time and Embedded systems (MARTE) design specifications; and secondly, such models are transformed to automatically solvable performance models by using QVT. This paper reports on our first experiences when implementing these two initial activities.
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Notes
- 1.
From this point on, we will use the generic term system designers to refer to any stakeholder taking advantage of our approach.
- 2.
We have indicated with a gray background the activities that are different from those in Fig. 1.
- 3.
Allocate can only be applied to Abstractions, which are a specific kind of UML Dependency.
- 4.
In fact, the QVT specification defines three transformation languages: Core, Operational and Relations, being the main difference among them their declarative or imperative nature.
- 5.
Sensor here represents the software element used to access hardware Sensors.
- 6.
- 7.
We based the encryption and decryption on an open source version of the Advanced Encryption Standard (AES) [8].
- 8.
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Gómez, A., Smith, C.U., Spellmann, A., Cabot, J. (2018). Enabling Performance Modeling for the Masses: Initial Experiences. In: Khendek, F., Gotzhein, R. (eds) System Analysis and Modeling. Languages, Methods, and Tools for Systems Engineering. SAM 2018. Lecture Notes in Computer Science(), vol 11150. Springer, Cham. https://doi.org/10.1007/978-3-030-01042-3_7
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