Auto Code Comment Assessment for Online Judge using Word Embedding and Word Mover's Distance
Authors: 
Rosa Ariani Sukamto, Muhammad Nabillah Fihira Rischa, Erna Piantari, Yudi Wibisono, Rani MegasariAuthors Info & Claims
Rosa Ariani Sukamto, Muhammad Nabillah Fihira Rischa, Erna Piantari, Yudi Wibisono, Rani MegasariAuthors Info & ClaimsPages 345 - 349
Abstract
Comments in source code are a form of inline documentation created by programmers to help others understand the function of the program. The students of the basic programming subject need how to learn to write better code comments which can be difficulties for the lecturer assessing. Therefore, the author proposes an automatic source code comment assessment method for the online judge system with a corpus-based text similarity approach. Word2vec, GloVe, and fastText models will be used to train word vectors with the Indonesian Wikipedia Dump. The Similarities will be measured using Word Mover's Distance (WMD). Experiments were carried out using epoch variations during the training process. Spearman's rho correlation coefficient, mean average error (MAE), and performance measurements of each model will be compared. The methods with the proposed word embedding approach still provide not good results.
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Published In
November 2022
415 pages
ISBN:9781450397902
DOI:10.1145/3575882
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Published: 27 February 2023
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IC3INA 2022
IC3INA 2022: The 2022 International Conference on Computer, Control, Informatics and Its Applications
November 22 - 23, 2022
Virtual Event, Indonesia
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