Recently, pre-trained programming language models such as Code-BERT have demonstrated substantial gains in code search. Despite their success, they rely on the availability of large amounts of parallel data to fine-tune the semantic mappings between queries and code. This restricts their practicality in domain-specific languages with relatively scarce and expensive data. In this paper, we propose CDCS, a novel approach for domain-specific code search. CDCS employs a transfer learning framework where an initial program representation model is pre-trained on a large corpus of common programming languages (such as Java and Python), and is further adapted to domain-specific languages such as Solidity and SQL. Un-like cross-language CodeBERT, which is directly fine-tuned in the target language, CDCS adapts a few-shot meta-learning algorithm called MAML to learn the good initialization of model parameters, which can be best reused in a domain-specific language. We evaluate the proposed approach on two domain-specific languages, namely Solidity and SQL, with model transferred from two widely used languages (Python and Java). Experimental results show that CDCS significantly outperforms conventional pre-trained code models that are directly fine-tuned in domain-specific languages, and it is particularly effective for scarce data.

@inproceedings{ChaiZSG22,
  title = {Cross-domain deep code search with meta learning},
  author = {Yitian Chai and Hongyu Zhang and Beijun Shen and Xiaodong Gu},
  booktitle = {ICSE},
  year = {2022},
  doi = {10.1145/3510003.3510125}
}