Using Ensemble Inference to Improve Recall of Clone Detection

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.