Seq2Seq模型在纠错码中的应用
Application of Seq2Seq Models on Code Correction
摘要：
在SARD的Juliet Test Suite for C/C++和Java测试集上，将不同的seq 2seq模型应用于程序设计语言的纠错任务，获得了75%（C/C++）和56%（Java）的修复率。我们在这些seq 2seq模型中引入了金字塔编码器，这大大提高了计算效率和内存效率，同时保持了与非金字塔模型相似的修复率。利用迁移学习和在Juliet Test Suite上预先训练好的模型，成功地对ITC基准测试样本（只有685个代码实例）进行了错误类型分类，为处理小型程序设计语言数据集提供了一种新的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
