Indiana University-Purdue University Indianapolis (IUPUI) / 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-speci c
modeling tools.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/12287 |
| Date | 09 March 2017 |
| Creators | Nair, Arvind |
| Contributors | Hill, James Haswell, Ning, Xia N., Raje, Rajeev R., Fang, Shiaofen |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Rights | Attribution 3.0 United States, http://creativecommons.org/licenses/by/3.0/us/ |
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