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.

本文提出了一种名为 ZC3 的新方法，用于零射击交叉语言代码克隆检测，通过对比片段预测、领域感知学习和循环一致性学习来生成在不同编程语言之间对齐且区分不同克隆类型的表示，实验证明 ZC3 相对于现有基准模型在 MAP 评分上分别提高了 67.12％，51.39％，14.85％和 53.01％。

ZC3：零样本跨语言代码克隆检测

ZC3: Zero-Shot Cross-Language Code Clone Detection

We consider the clone detection and information retrieval problems for source
code, well-known tasks important for any programming language. Although it is
also an important and interesting problem to find code snippets that operate
identically but are written in different programming languages, to the best of
our knowledge multilingual clone detection has not been studied in literature.
In this work, we formulate the multilingual clone detection problem and present
XCD, a new benchmark dataset produced from the CodeForces submissions dataset.
Moreover, we present a novel training procedure, called cross-consistency
training (CCT), that we apply to train language models on source code in
different programming languages. The resulting CCT-LM model, initialized with
GraphCodeBERT and fine-tuned with CCT, achieves new state of the art,
outperforming existing approaches on the POJ-104 clone detection benchmark with
95.67\% MAP and AdvTest code search benchmark with 47.18\% MRR; it also shows
the best results on the newly created multilingual clone detection benchmark
XCD across all programming languages.

本文提出了一个新的多语言代码克隆检测问题，以及一种名为 cross-consistency training (CCT) 的新型训练程序和一种 CCT-LM 语言模型，该模型在不同编程语言的源代码上训练，在 POJ-104 代码克隆检测基准测试中实现了 95.67％的 MAP 和 AdvTest 代码搜索基准测试中的 47.18％MRR 最佳结果，并在新创建的多语言代码克隆检测基准测试 XCD 中展现出最好的成绩。