Abstract. Model-Driven Engineering has been promoted for some time as the solution for the main problem software industry is facing, i.e. complexity of software development, by raising the abstraction level and introducing more automation in the process. The promises are many; among them improved software quality by increased traceability between artifacts, early defect detection, reducing manual and error-prone work and including knowledge in generators. However, in our opinion MDE is still in the early adoption phase and to be successfully adopted by industry, it must prove its superiority over other development paradigms and be supported by a rich ecosystem of stable, compatible and standardized tools. It should also not introduce more complexity than it removes. The subject of this paper is the challenges in MDE adoption from our experience of using MDE in real and research projects, where MDE has potential for success and what the key success criteria are.
Model Driven Engineering (MDE) has to deal with an increasing number of interrelated modelling artefacts. The Model Driven Performance Engineering (MDPE) process is one concrete illustration of such a situation. This process applies MDE within the context of performance engineering in order to support domain experts, who generally lack the necessary performance expertise. In this paper, we demonstrate the use of megamodelling to manage the numerous artefacts involved in MDPE. Megamodelling enables the explicit modelling of the metadata on MDE artefacts, including possible relationships between those artefacts. Appropriate tool support enables different stakeholders to exploit this additional information. Applying the megamodelling to MDPE pointed out the need for an extension of the existing approach. Thus, the result of the paper is twofold: first, an extension of megamodelling is proposed, second the benefits of the approach are shown on the MDPE use case. We claim that the extension is not solely useful for the latter case, but has a more generic applicability.
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