Cross-Domain Deep Code Search with Meta Learning

TL;DR

CroCS introduces a meta-learning based transfer approach for domain-specific code search, enabling effective adaptation of pre-trained models to languages with limited data, outperforming traditional fine-tuning methods.

Contribution

The paper presents CroCS, a novel meta-learning framework that improves domain-specific code search by adapting pre-trained models to languages with scarce data using MAML.

Findings

CroCS significantly outperforms traditional fine-tuning in domain-specific languages.

It is especially effective when data is scarce.

Experimental results on SQL and Solidity demonstrate its superiority.

Abstract

Recently, pre-trained programming language models such as CodeBERT have demonstrated substantial gains in code search. Despite showing great performance, 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 CroCS, a novel approach for domain-specific code search. CroCS 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 SQL and Solidity. Unlike cross-language CodeBERT, which is directly fine-tuned in the target language, CroCS 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, SQL and Solidity, 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.