Abstract
Our work investigates using semantic indicators in source code to generate descriptions of its functionality. We applied natural language processing (NLP) techniques to improve the performance of a neural machine translation (NMT) model on source code. We trained the model on 20,000 code-comment pairs from the Source Code Analysis Dataset (SCAD) using a custom Python source code tokenizer and a learning model with attention, RNNs, LSTMs, and auto-encoders. We evaluated the model’s performance on a hold-out dataset using several established translation metrics, and found that our methods outperform prior work with a mean Bilingual Evaluation Understudy (BLEU) score of 0.47.
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