Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings
Abstract
Neural program embeddings have demonstrated considerable promise in a range of program analysis tasks, including clone identification, program repair, code completion, and program synthesis. However, most existing methods generate neural program embeddings directly from the program source codes, by learning from features such as tokens, abstract syntax trees, and control flow graphs.
BibTeX
@inproceedings{Li-al:ICSE22,
author = {Zongjie Li and
Pingchuan Ma and
Huaijin Wang and
Shuai Wang and
Qiyi Tang and
Sen Nie and
Shi Wu},
title = {Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings},
booktitle = {ICSE},
pages = {2253--2265},
publisher = {{ACM}},
year = {2022},
}