Kyeong-Seok Hyun, Woosung Choi, Jaehwa Chung

KIPS Transactions on Software and Data Engineering, Vol. 11, No. 11, pp. 473-478, Nov. 2022
https://doi.org/10.3745/KTSDE.2022.11.11.473,   PDF Download:
Keywords: Deep Learing, GRU, FiLM, Online Judge
Abstract

Evaluation learning based on code testing is becoming a popular solution in programming education via Online judge(OJ). In the recent past, many papers have been published on how to detect plagiarism through source code similarity analysis to support OJ. However, deep learning-based research to support automated tutoring is insufficient. In this paper, we propose Input & Output side FiLM models to predict whether the input code will pass or fail. By applying Feature-wise Linear Modulation(FiLM) technique to GRU, our model can learn combined information of Java byte codes and problem information that it tries to solve. On experimental design, a balanced sampling technique was applied to evenly distribute the data due to the occurrence of asymmetry in data collected by OJ. Among the proposed models, the Input Side FiLM model showed the highest performance of 73.63%. Based on result, it has been shown that students can check whether their codes will pass or fail before receiving the OJ evaluation which could provide basic feedback for improvements.

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