AUTHOR=Huang Shan , Zhou Xiao , Chin Sang 

TITLE=Application of Seq2Seq Models on Code Correction

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 4 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.590215

DOI=10.3389/frai.2021.590215

ISSN=2624-8212

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. keyword: programing language correction, seq2seq, pyramid encoder, attention mechanism, transfer learning