Using recommender systems to improve proactive modeling
This article investigates using recommender systems within graphical domain-specific modeling languages (DSMLs). The objective of using recommender systems within a graphical DSML is to overcome a shortcoming of proactive modeling where the modeler must inform the model intelligence engine how to pr...
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Veröffentlicht in: | Software and systems modeling 2021-08, Vol.20 (4), p.1159-1181 |
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creator | Nair, Arvind Ning, Xia Hill, James H. |
description | This article investigates using recommender systems within graphical domain-specific modeling languages (DSMLs). The objective of using recommender systems within a graphical DSML is to overcome a shortcoming of proactive modeling where the modeler must inform the model intelligence engine how to progress when it cannot automatically determine the next modeling action to execute (e.g., add, delete, or edit). To evaluate our objective, we implemented a recommender system into the Proactive Modeling Engine, which is an add-on for the Generic Modeling Environment. We then conducted experiments to subjectively and objectively evaluate enhancements to the Proactive Modeling Engine. The results of our experiments show that extending proactive modeling with a recommender system results in an average reciprocal hit-rank of 0.871. Likewise, the enhancements yield a System Usability Scale rating of 77. Finally, user feedback shows that integrating recommender systems into DSMLs increases usability and learnability. |
doi_str_mv | 10.1007/s10270-020-00841-2 |
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subjects | Compilers Computer Science Environment models Information Systems Applications (incl.Internet) Interpreters IT in Business Programming Languages Programming Techniques Recommender systems Regular Paper Software Engineering Software Engineering/Programming and Operating Systems Usability |
title | Using recommender systems to improve proactive modeling |
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