Optimizing Pre-trained Code Embeddings with Triplet Loss for Code Smell Detection

Code embedding represents code semantics in vector form. While existing code embedding methods have been successfully employed for various source code analysis tasks, further studies are needed to achieve the performance and functionality of static code analysis tools and improve code embedding for...

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Vydané v:	IEEE access Ročník 13; s. 1
Hlavní autori:	Nizam, Ali, Islamoglu, Ertugrul, Adali, Omer Kerem, Aydin, Musa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Analytical models Classification Code embedding code smell detection Codes Contrastive learning Deep learning Effectiveness Embedding Image processing Measurement Optimization Programming languages Python Semantics Software Source code Source coding Static code analysis Syntactics Transformers triplet loss Vectors
ISSN:	2169-3536, 2169-3536
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