  Software clones are beneficial to detect security gaps and software
maintenance in one programming language or across multiple languages. The
existing work on source clone detection performs well but in a single
programming language. However, if a piece of code with the same functionality
is written in different programming languages, detecting it is harder as
different programming languages have a different lexical structure. Moreover,
most existing work rely on manual feature engineering. In this paper, we
propose a deep neural network model based on source code AST embeddings to
detect cross-language clones in an end-to-end fashion of the source code
without the need of the manual process to pinpoint similar features across
different programming languages. To overcome data shortage and reduce
overfitting, a Siamese architecture is employed. The design methodology of our
model is twofold -- (a) it accepts AST embeddings as input for two different
programming languages, and (b) it uses a deep neural network to learn abstract
features from these embeddings to improve the accuracy of cross-language clone
detection. The early evaluation of the model observes an average precision,
recall and F-measure score of $0.99$, $0.59$ and $0.80$ respectively, which
indicates that our model outperforms all available models in cross-language
clone detection.
