A study of machine learning approaches to cross-language code clone detection

While clone detection across programs written in the same programming language has been studied extensively in the literature, the task of detecting clones across multiple programming languages is not covered as well, and approaches based on comparison cannot be directly applied. In this thesis, we present a clone detection method based on supervised machine learning able to detect clone across programming languages. Our method uses an unsupervised learning approach to learn tokenlevel vector representations and an LSTM-based neural network to predict if two code fragments are clones. To train our network, we present a cross-language code clone dataset — which is to the best of our knowledge the first of its kind — containing more than 50000 code fragments written in Python and Java. We show that our method is able to detect code clones between Python and Java. We also compare our method to state-of-the-art tools in single-language clone detection and show we achieve better F1-score.

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