  Developers introduce code clones to improve programming productivity. Many
existing studies have achieved impressive performance in monolingual code clone
detection. However, during software development, more and more developers write
semantically equivalent programs with different languages to support different
platforms and help developers translate projects from one language to another.
Considering that collecting cross-language parallel data, especially for
low-resource languages, is expensive and time-consuming, how designing an
effective cross-language model that does not rely on any parallel data is a
significant problem. In this paper, we propose a novel method named ZC3 for
Zero-shot Cross-language Code Clone detection. ZC3 designs the contrastive
snippet prediction to form an isomorphic representation space among different
programming languages. Based on this, ZC3 exploits domain-aware learning and
cycle consistency learning to further constrain the model to generate
representations that are aligned among different languages meanwhile are
diacritical for different types of clones. To evaluate our approach, we conduct
extensive experiments on four representative cross-language clone detection
datasets. Experimental results show that ZC3 outperforms the state-of-the-art
baselines by 67.12%, 51.39%, 14.85%, and 53.01% on the MAP score, respectively.
We further investigate the representational distribution of different languages
and discuss the effectiveness of our method.
