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 bibliografia

Vydané v:	Proceedings / International Conference on Software Engineering s. 769 - 781
Hlavní autori:	Yan, Yanfu, Duong, Viet, Shao, Huajie, Poshyvanyk, Denys
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 26.04.2025
Predmet:	Code Models Codes Contrastive learning Data models Measurement OOD detection Predictive models Reliability Software engineering Testing Training Training data Trustworthy ML
ISSN:	1558-1225
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