Towards More Trustworthy Deep Code Models by Enabling Out-of-Distribution Detection

Numerous machine learning (ML) models have been developed, including those for software engineering (SE) tasks, under the assumption that training and testing data come from the same distribution. However, training and testing distributions often differ, as training datasets rarely encompass the ent...

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Bibliographic Details

Published in:	Proceedings / International Conference on Software Engineering pp. 769 - 781
Main Authors:	Yan, Yanfu, Duong, Viet, Shao, Huajie, Poshyvanyk, Denys
Format:	Conference Proceeding
Language:	English
Published:	IEEE 26.04.2025
Subjects:	Code Models Codes Contrastive learning Data models Measurement OOD detection Predictive models Reliability Software engineering Testing Training Training data Trustworthy ML
ISSN:	1558-1225
Online Access:	Get full text
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