A Study of Pyramid Structure for Code Correction

01/28/2020
by   Shan Huang, et al.
0

We demonstrate the implementations of pyramid encoders in both multi-layer GRU and Transformer for seq2seq tasks. We apply the models to the code correction task on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Dataset(SARD), successfully repaired 90.1% of faulted code in the test dataset, and show that a pyramid structure can greatly improve memory efficiency and therefore computation efficiency. We successfully carried 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 datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/23/2022

Integrally Pre-Trained Transformer Pyramid Networks

In this paper, we present an integral pre-training framework based on ma...
research
10/13/2020

Pagsusuri ng RNN-based Transfer Learning Technique sa Low-Resource Language

Low-resource languages such as Filipino suffer from data scarcity which ...
research
01/09/2023

Transfer learning for conflict and duplicate detection in software requirement pairs

Consistent and holistic expression of software requirements is important...
research
08/20/2022

An ensemble meta-estimator to predict source code testability

Unlike most other software quality attributes, testability cannot be eva...
research
08/26/2023

Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid Algorithm with Transformer and CNN Encoders

Transfer learning improves the performance of deep learning models by in...
research
05/17/2022

When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning

Transfer learning (TL) in natural language processing (NLP) has seen a s...
research
04/07/2017

Convolutional Neural Pyramid for Image Processing

We propose a principled convolutional neural pyramid (CNP) framework for...

Please sign up or login with your details

Forgot password? Click here to reset