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Competitive programming remains a very popular activity that combines bothsoftware engineering and education. In order to prepare and to practice,contestants use extensive archives of problems from past contents available onvarious competitive programming platforms. One way to make this process moreeffective is to provide an automatic tag system for the tasks. Prior works dothat by either using the tasks' problem statements or the code of theirsolutions.  In this study, we investigate which information source is more valuable fortag prediction. To answer that question, we compare existing approaches of bothtypes on the same dataset and with the same set of tags. Then, we propose anovel approach, which is an ensemble of the Gated Graph Neural Network modelfor analyzing solutions and the Bidirectional Encoder Representations fromTransformers model for processing statements. Our experiments show that ourapproach outperforms previously proposed models by 0.175 of the PR-AUC metric.

Predicting Tags For Programming Tasks by Combining Textual And Source Code Data