Code Clone Detection, which aims to retrieve functionally similar programs
from large code bases, has been attracting increasing attention. Modern
software often involves a diverse range of programming languages. However,
current code clone detection methods are generally limited to only a few
popular programming languages due to insufficient annotated data as well as
their own model design constraints. To address these issues, we present AdaCCD,
a novel cross-lingual adaptation method that can detect cloned codes in a new
language without any annotations in that language. AdaCCD leverages
language-agnostic code representations from pre-trained programming language
models and propose an Adaptively Refined Contrastive Learning framework to
transfer knowledge from resource-rich languages to resource-poor languages. We
evaluate the cross-lingual adaptation results of AdaCCD by constructing a
multilingual code clone detection benchmark consisting of 5 programming
languages. AdaCCD achieves significant improvements over other baselines, and
it is even comparable to supervised fine-tuning.

AdaCCD 是一种新颖的跨语言适应方法，可以在新的语言中检测克隆代码，而无需任何该语言的注释。AdaCCD 利用预训练的编程语言模型中的语言无关的代码表示，并提出了自适应精炼对比学习框架，以将知识从资源丰富的语言传递到资源匮乏的语言。通过构建由 5 种编程语言组成的多语言代码克隆检测基准集，我们评估了 AdaCCD 的跨语言适应结果。与其他基线方法相比，AdaCCD 取得了显著的改进，甚至可以与监督微调方法相媲美。