  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 https://github.com/jorge-martinez-gil/ensemble-codesim.
