Advanced Ensemble Learning for Accurate and Interpretable Source Code Similarity Assessment

An ensemble learning approach that combines multiple unsupervised similarity measures can outperform individual techniques and rival state-of-the-art supervised methods, especially in scenarios with limited training data, while maintaining interpretability and reducing the environmental impact.