Hierarchical semantic-aware neural code representation
Code representation is a fundamental problem in many software engineering tasks. Despite the effort made by many researchers, it is still hard for existing methods to fully extract syntactic, structural and sequential features of source code, which form the hierarchical semantics of the program and...
Saved in:
Main Authors: | , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2022
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/8766 https://ink.library.smu.edu.sg/context/sis_research/article/9769/viewcontent/jss22.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-9769 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-97692024-05-23T05:39:37Z Hierarchical semantic-aware neural code representation JIANG, Yuan SU, Xiaohong TREUDE, Christoph WANG, Tiantian Code representation is a fundamental problem in many software engineering tasks. Despite the effort made by many researchers, it is still hard for existing methods to fully extract syntactic, structural and sequential features of source code, which form the hierarchical semantics of the program and are necessary to achieve a deeper code understanding. To alleviate this difficulty, we propose a new supervised approach based on the novel use of Tree-LSTM to incorporate the sequential and the global semantic features of programs explicitly into the representation model. Unlike previous techniques, our proposed model can not only learn low-level syntactic information within each statement but also the high-level semantic information between statements over the constructed semantic graph. Besides, considering that the sequential semantics is also critical for developers to understand the dependency path and data flow transmission, we propose a DFS-based method to generate the topological order of statements being processed, and then feed them as well as their in-neighboring information and syntactic embeddings into the proposed model to learn richer statement-level semantic features. Extensive experiments on multiple program comprehension tasks, e.g., code clone detection, demonstrate that our method achieves promising performance compared with other existing baselines. 2022-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8766 info:doi/10.1016/j.jss.2022.111355 https://ink.library.smu.edu.sg/context/sis_research/article/9769/viewcontent/jss22.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Code representation Graph-LSTM Hierarchical semantics Program classification Clone detection Vulnerability detection Deep learning Databases and Information Systems Graphics and Human Computer Interfaces Software Engineering |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Code representation Graph-LSTM Hierarchical semantics Program classification Clone detection Vulnerability detection Deep learning Databases and Information Systems Graphics and Human Computer Interfaces Software Engineering |
spellingShingle |
Code representation Graph-LSTM Hierarchical semantics Program classification Clone detection Vulnerability detection Deep learning Databases and Information Systems Graphics and Human Computer Interfaces Software Engineering JIANG, Yuan SU, Xiaohong TREUDE, Christoph WANG, Tiantian Hierarchical semantic-aware neural code representation |
description |
Code representation is a fundamental problem in many software engineering tasks. Despite the effort made by many researchers, it is still hard for existing methods to fully extract syntactic, structural and sequential features of source code, which form the hierarchical semantics of the program and are necessary to achieve a deeper code understanding. To alleviate this difficulty, we propose a new supervised approach based on the novel use of Tree-LSTM to incorporate the sequential and the global semantic features of programs explicitly into the representation model. Unlike previous techniques, our proposed model can not only learn low-level syntactic information within each statement but also the high-level semantic information between statements over the constructed semantic graph. Besides, considering that the sequential semantics is also critical for developers to understand the dependency path and data flow transmission, we propose a DFS-based method to generate the topological order of statements being processed, and then feed them as well as their in-neighboring information and syntactic embeddings into the proposed model to learn richer statement-level semantic features. Extensive experiments on multiple program comprehension tasks, e.g., code clone detection, demonstrate that our method achieves promising performance compared with other existing baselines. |
format |
text |
author |
JIANG, Yuan SU, Xiaohong TREUDE, Christoph WANG, Tiantian |
author_facet |
JIANG, Yuan SU, Xiaohong TREUDE, Christoph WANG, Tiantian |
author_sort |
JIANG, Yuan |
title |
Hierarchical semantic-aware neural code representation |
title_short |
Hierarchical semantic-aware neural code representation |
title_full |
Hierarchical semantic-aware neural code representation |
title_fullStr |
Hierarchical semantic-aware neural code representation |
title_full_unstemmed |
Hierarchical semantic-aware neural code representation |
title_sort |
hierarchical semantic-aware neural code representation |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2022 |
url |
https://ink.library.smu.edu.sg/sis_research/8766 https://ink.library.smu.edu.sg/context/sis_research/article/9769/viewcontent/jss22.pdf |
_version_ |
1814047522962800640 |