Integrating Recommender Systems into Domain Specific Modeling Tools
This thesis investigates integrating recommender systems into model-driven engineering tools powered by domain-specific modeling languages. The objective of integrating recommender systems into such tools is 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 integrated a recommender system into the Proactive Modeling Engine, which is a add-on for the Generic Modeling Environment (GME). We then conducted experiments to both subjective and objectively evaluate the enhancements to the Proactive Modeling Engine. The results of our experiments show that integrating recommender system into the Proactive Modeling Engine results in an Average Reciprocal Hit-Rank (ARHR) of 0.871. Likewise, the integration results in System Usability Scale (SUS) rating of 77. Finally, user feedback shows that the integration of the recommender system to the Proactive Modeling Engine increases the usability and learnability of domain-specic modeling tools.
Hill, Purdue University.
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