KAPE: kNN-based performance testing for deep code search
Code search is a common yet important activity of software developers. An efficient code search model can largely facilitate the development process and improve the programming quality. Given the superb performance of learning the contextual representations, deep learning models, especially pre-trai...
Saved in:
Main Authors: | GUO, Yuejun, HU, Qiang, XIE, Xiaofei, MAXIME, Cordy, PAPADAKIS, Mike, LE TRAON, Yves |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2023
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/9093 https://ink.library.smu.edu.sg/context/sis_research/article/10096/viewcontent/3624735.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
An empirical study on data distribution-aware test selection for deep learning enhancement
by: HU, Qiang, et al.
Published: (2022) -
Aries: Efficient testing of deep neural networks via labeling-free accuracy estimation
by: HU, Qiang, et al.
Published: (2023) -
Test optimization in DNN testing: A survey
by: HU, Qiang, et al.
Published: (2024) -
GraphCode2Vec: Generic code embedding via lexical and program dependence analyses
by: MA, Wei, et al.
Published: (2022) -
Revisiting neuron coverage metrics and quality of deep neural networks
by: YANG, Zhou, et al.
Published: (2022)