| Field |
Value |
Language |
|
dc.contributor.author |
Wan, Y |
|
|
dc.contributor.author |
Bi, Z |
|
|
dc.contributor.author |
He, Y |
|
|
dc.contributor.author |
Zhang, J |
|
|
dc.contributor.author |
Zhang, H |
|
|
dc.contributor.author |
Sui, Y
https://orcid.org/0000-0002-9510-6574
|
|
|
dc.contributor.author |
Xu, G
https://orcid.org/0000-0003-4493-6663
|
|
|
dc.contributor.author |
Jin, H |
|
|
dc.contributor.author |
Yu, P |
|
|
dc.date.accessioned |
2024-06-05T02:05:18Z |
|
|
dc.date.available |
2024-06-05T02:05:18Z |
|
|
dc.identifier.citation |
ACM Computing Surveys |
|
|
dc.identifier.issn |
0360-0300 |
|
|
dc.identifier.issn |
1557-7341 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/179406
|
|
|
dc.description.abstract |
<jats:p>Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://xcodemind.github.io). At last, we also point out several challenging and promising directions for future research.</jats:p> |
|
|
dc.language |
en |
|
|
dc.publisher |
Association for Computing Machinery (ACM) |
|
|
dc.relation.ispartof |
ACM Computing Surveys |
|
|
dc.relation.isbasedon |
10.1145/3664597 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.rights |
“©ACM2024. This is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 18 May 2024 https://doi.org/10.1145/3664597” |
|
|
dc.subject |
08 Information and Computing Sciences |
|
|
dc.subject.classification |
Information Systems |
|
|
dc.subject.classification |
46 Information and computing sciences |
|
|
dc.title |
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit |
|
|
dc.type |
Journal Article |
|
|
utslib.for |
08 Information and Computing Sciences |
|
|
pubs.organisational-group |
University of Technology Sydney |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre |
|
|
pubs.organisational-group |
University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.date.updated |
2024-06-05T02:05:16Z |
|
|
pubs.publication-status |
Published online |
|
