Application of seq2seq models on code correction
Files
First author draft
Date
2020
DOI
Authors
Chin, Sang
Huang, Shan
Zhou, Xiao
Version
First author draft
OA Version
Citation
S. Chin, S. Huang, X. Zhou. "Application of Seq2Seq Models on Code Correction."
https://arxiv.org/abs/2001.11367.
Abstract
We apply various seq2seq models on programming language correction tasks on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Datasets (SARD), and achieve 75%(for C/C++) and 56%(for Java) repair rates on these tasks. We introduce Pyramid Encoder in these seq2seq models, which largely increases the computational efficiency and memory efficiency, while remain similar repair rate to their non-pyramid counterparts. We successfully carry out error type classification task on ITC benchmark examples (with only 685 code instances) using transfer learning with models pre-trained on Juliet Test Suite, pointing out a novel way of processing small programing language datasets.