Abstract
Context. Performance and scalability are of critical value for distributed and multiuser systems like web applications. Posity is a model-driven development tool that allows software engineers to specify a set of graphical diagrams for the automatic generation of web and/or desktop software applications. Posity provides the benefits of model-driven engineering (MDE) tools in terms of high-quality code generation, implementation speed, support for traceability and debuggability, etc. However, web applications generated with Posity do not scale properly to satisfy unpredictable performance demands. As a result, Posity industrial adoption is hindered. Objective. Design a treatment for improving performance and scalability of web applications generated with Posity. Method. We investigate current problems of web applications generated with Posity. Results from our investigation suggest candidate architectures, which we evaluate by applying the architecture trade-off analysis method (ATAM). The outcome of the ATAM evaluation guides the design and implementation of a thick-client architecture for the Posity runtime environment for web applications; which we validate by means of a laboratory demonstration. Results. i) we contribute with criteria for selecting a proper architecture for solving performance and scalability problems, and ii) we report on the experience of designing, developing and validating an architecture for Posity runtime environment. Conclusions. Results from the laboratory demonstration show tangible improvements in terms of performance and scalability of web applications generated by Posity. These advancements are promising and motivate further development of the thick-client architecture for Posity runtime environment for web applications. This experience report concludes with lessons learnt on promoting the adoption of model-driven development tools.
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Moretti, G., Ruiz, M., Spielberger, J. (2020). Improving Performance and Scalability of Model-Driven Generated Web Applications. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_17
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