TrackMine: Topic Tracking in Model Mining using Genetic Algorithm

With the advance of Model-Driven Engineering (MDE), number of generated models has grown exponentially. From the one hand, to provide support for software development using MDE, we need to store various models in the model repositories. From the other hand, extracting a particular model of interest from model repositories is a major challenge. One of the solutions is to use topic tracking in model mining. This provides a rapid and robust exploration that aligns well with the process of finding desired models in complex and large model repositories. In this paper, we propose TrackMine, a genetic algorithm based on topic tracking, to effectively sort the arrangement of models in a repository. This can help modelers realize the tracking, usage, and evolution of the models. TrackMine creates an optimized list of models by rearranging the order of models using user-defined similarity metrics. We demonstrate the applicability of our approach through a proof of concept implementation and evaluate the benefits of the presented algorithm using three different datasets. The experimental results demonstrate the practicality and suitability of our approach for model mining.