  Code clone is a serious problem in software and has the potential to software
defects, maintenance overhead, and licensing violations. Therefore, clone
detection is important for reducing maintenance effort and improving code
quality during software evolution. A variety of clone detection techniques have
been proposed to identify similar code in software. However, few of them can
efficiently detect semantic clones (functionally similar code without any
syntactic resemblance). Recently, several deep learning based clone detectors
are proposed to detect semantic clones. However, these approaches have high
cost in data labelling and model training. In this paper, we propose a novel
approach that leverages word embedding and ensemble learning techniques to
detect semantic clones. Our evaluation on a commonly used clone benchmark,
BigCloneBench, shows that our approach significantly improves the precision and
recall of semantic clone detection, in comparison to a token-based clone
detector, SourcererCC, and another deep learning based clone detector, CDLH.
