Multi-label Classification for Automatic Tag Prediction in the Context of Programming Challenges
This work addresses the tedious task of manual tagging for problem creators in programming challenge platforms, though it is incremental as it builds on existing deep learning techniques.
The paper tackled the problem of automatically tagging programming challenge descriptions by comparing machine and deep learning methods against traditional IR approaches like tf-idf, finding that deep learning methods outperformed these baseline methods.
One of the best ways for developers to test and improve their skills in a fun and challenging way are programming challenges, offered by a plethora of websites. For the inexperienced ones, some of the problems might appear too challenging, requiring some suggestions to implement a solution. On the other hand, tagging problems can be a tedious task for problem creators. In this paper, we focus on automating the task of tagging a programming challenge description using machine and deep learning methods. We observe that the deep learning methods implemented outperform well-known IR approaches such as tf-idf, thus providing a starting point for further research on the task.