Multi-label Classification for Automatic Tag Prediction in the Context of Programming Challenges

TL;DR

This paper presents a machine learning approach to automatically assign tags to programming challenge descriptions, demonstrating that deep learning methods outperform traditional IR techniques like tf-idf in this task.

Contribution

It introduces a deep learning-based method for automatic tagging of programming challenges, improving over existing IR approaches.

Findings

Deep learning methods outperform tf-idf in tag prediction accuracy.

Automated tagging can assist problem creators and learners.

The approach provides a foundation for further research in automated challenge tagging.

Abstract

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.