CLCD-I: Cross-Language Clone Detection by Using Deep Learning with InferCode

Abstract

Source code clones are common in software development as part of reuse practice. However, they are also often a source of errors compromising software maintainability. The existing work on code clone detection mainly focuses on clones in a single programming language. However, nowadays software is increasingly developed on a multilanguage platform on which code is reused across different programming languages. Detecting code clones in such a platform is challenging and has not been studied much. In this paper, we present CLCD-I, a deep neural network-based approach for detecting cross-language code clones by using InferCode which is an embedding technique for source code. The design of our model is twofold: (a) taking as input InferCode embeddings of source code in two different programming languages and (b) forwarding them to a Siamese architecture for comparative processing. We compare the performance of CLCD-I with LSTM autoencoders and the existing approaches on cross-language code clone detection. The evaluation shows the CLCD-I outperforms LSTM autoencoders by 30% on average and the existing approaches by 15% on average.