  Large-scale source-code clone detection is a challenging task. In our
previous work, we proposed an approach (SSCD) that leverages artificial neural
networks and approximates nearest neighbour search to effectively and
efficiently locate clones in large-scale bodies of code, in a time-efficient
manner. However, our literature review suggests that the relative efficacy of
differing neural network models has not been assessed in the context of
large-scale clone detection approaches. In this work, we aim to assess several
such models individually, in terms of their potential to maximize recall, while
preserving a high level of precision during clone detection. We investigate if
ensemble inference (in this case, using the results of more than one of these
neural network models in combination) can further assist in this task.
  To assess this, we employed four state-of-the-art neural network models and
evaluated them individually/in combination. The results, on an illustrative
dataset of approximately 500K lines of C/C++ code, suggest that ensemble
inference outperforms individual models in all trialled cases, when recall is
concerned. Of individual models, the ADA model (belonging to the ChatGPT family
of models) has the best performance. However commercial companies may not be
prepared to hand their proprietary source code over to the cloud, as required
by that approach. Consequently, they may be more interested in an
ensemble-combination of CodeBERT-based and CodeT5 models, resulting in similar
(if slightly lesser) recall and precision results.
