The capability of accurately determining code similarity is crucial in many
tasks related to software development. For example, it might be essential to
identify code duplicates for performing software maintenance. This research
introduces a novel ensemble learning approach for code similarity assessment,
combining the strengths of multiple unsupervised similarity measures. The key
idea is that the strengths of a diverse set of similarity measures can
complement each other and mitigate individual weaknesses, leading to improved
performance. Preliminary results show that while Transformers-based CodeBERT
and its variant GraphCodeBERT are undoubtedly the best option in the presence
of abundant training data, in the case of specific small datasets (up to 500
samples), our ensemble achieves similar results, without prejudice to the
interpretability of the resulting solution, and with a much lower associated
carbon footprint due to training. The source code of this novel approach can be
downloaded from this https URL

该研究介绍了一种新的集成学习方法来评估代码相似性，结合多种无监督相似度度量的优点。初步结果表明，尽管基于 Transformers 的 CodeBERT 和其变种 GraphCodeBERT 在有充足训练数据的情况下是最佳选择，但在特定的小数据集（最多 500 个样本）的情况下，我们的集成方法达到了类似的结果，而且结果解释性较强，与训练相关的碳足迹明显较低。