- Title
- Cross-Domain Deep Code Search with Meta Learning
- Creator
- Chai, Yitian; Zhang, Hongyu; Shen, Beijun; Gu, Xiaodong
- Relation
- ICSE '22: 44th International Conference on Software Engineering. Proceedings of the 44th International Conference on Software Engineering (Pittsburgh, PA 21-29 May, 2022) p. 487-498
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2022
- Description
- Recently, pre-trained programming language models such as CodeBERT 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. Unlike 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.
- Subject
- code search; pre-trained code models; meta learning; few-shot learning; deep learning
- Identifier
- http://hdl.handle.net/1959.13/1465605
- Identifier
- uon:47315
- Identifier
- ISBN:9781450392211
- Language
- eng
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