Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection

Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection

Code embedding represents code semantics in vector form. Although code embedding-based systems have been successfully applied to various source code analysis tasks, further research is required to enhance code embedding for better code analysis capabilities, aiming to surpass the performance and fun...

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Bibliographic Details
Main Authors: Ali Nizam, Ertugrul Islamoglu, Omer Kerem Adali, Musa Aydin
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Code embedding
contrastive learning
triplet loss
code smell detection
Online Access:https://ieeexplore.ieee.org/document/10890964/
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https://ieeexplore.ieee.org/document/10890964/

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