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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Podrobná bibliografie

Vydáno v:	Proceedings / International Conference on Software Engineering s. 769 - 781
Hlavní autoři:	Yan, Yanfu, Duong, Viet, Shao, Huajie, Poshyvanyk, Denys
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 26.04.2025
Témata:	Code Models Codes Contrastive learning Data models Measurement OOD detection Predictive models Reliability Software engineering Testing Training Training data Trustworthy ML
ISSN:	1558-1225
On-line přístup:	Získat plný text
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