Addressing the Rare Word Problem in Source Code Modelling
Year: 2020
Type: dissertation
Abstract: The field of automatic program repair has adapteddeep learning techniques. Sequence to sequence neural networkshave successfully been applied in neural machine translation(NMT). This can also be applied to automatic program repair,attempting to translate buggy source code into fixed sourcecode. However, the frequent occurrence of user-defined variablesmakes the rare word problem a significant issue. Techniquesused in NMT to handle the rare word problem specifically bytepairing encoding (BPE) and the copy mechanism were appliedand evaluated on source code. The results showed that whenobserving the exact sequence match of the predicted output andtarget output, techniques were not an improvement. However,when observing correct syntax techniques outperformed theoriginal model without any techniques applied. To be able tosee an improvement in exact sequence match there should be agreater variety of sequence length and vocabulary size also, moreextensive hyperparameter tuning should be performed.
Author: Ivstam, Linn
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7035624500