Pathways to Leverage Transcompiler based Data Augmentation for Cross-Language Clone Detection

Software clones are often introduced when developers reuse code fragments to implement similar functionalities in the same or different software systems resulting in duplicated fragments or code clones in those systems. Due to the adverse effect of clones on software maintenance, a great many tools and techniques and techniques have appeared in the literature to detect clones. Many high-performing clone detection tools today are based on deep learning techniques and are mostly used for detecting clones written in the same programming language, whereas clone detection tools for detecting cross-language clones are also emerging rapidly. The popularity of deep learning-based clone detection tools creates an opportunity to investigate how known strategies that boost the performances of deep learning models could be further leveraged to improve the clone detection tools. In this paper, we investigate such a strategy, data augmentation, which has not yet been explored for cross-language clone detection as opposed to single language clone detection. We show how the existing knowledge on transcompilers (source-to-source translators) can be used for data augmentation to boost the performance of cross-language clone detection models, as well as to adapt single-language clone detection models to create cross-language clone detection pipelines. To demonstrate the performance boost for cross-language clone detection through data augmentation, we exploit Transcoder, which is a pre-trained source-to-source translator. To show how to extend single-language models for cross-language clone detection, we extend a popular single-language model, Graph Matching Network (GMN), in a combination with the transcompilers and code parsers (srcML). We evaluated our models on popular benchmark datasets. Our experimental results showed improvements in F1 scores (sometimes up to 3%) for the cutting-edge cross-language clone detection models. Even when extending GMN for cross-language clone detection, the models built leveraging data augmentation outperformed the baseline with scores of 0.90, 0.92, and 0.91 for precision, recall, and F1 score, respectively.