Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2022

Abstract

Due to the emergence of large-scale codebases, such as GitHub and Gitee, searching and reusing existing code can help developers substantially improve software development productivity. Over the years, many code search tools have been developed. Early tools leveraged the information retrieval (IR) technique to perform an efficient code search for a frequently changed large-scale codebase. However, the search accuracy was low due to the semantic mismatch between query and code. In the recent years, many tools leveraged Deep Learning (DL) technique to address this issue. But the DL-based tools are slow and the search accuracy is unstable.In this paper, we presented an IR-based tool CodeMatcher, which inherits the advantages of the DL-based tool in query semantics matching. Generally, CodeMatcher builds indexing for a large-scale codebase at first to accelerate the search response time. For a given search query, it addresses irrelevant and noisy words in the query, then retrieves candidate code from the indexed codebase via iterative fuzzy search, and finally reranks the candidates based on two designed measures of semantic matching between query and candidates. We implemented CodeMatcher as a search engine website. To verify the effectiveness of our tool, we evaluated CodeMatcher on 41k+ open-source Java repositories. Experimental results showed that CodeMatcher can achieve an industrial-level response time (0.3s) with a common server with an Intel-i7 CPU. On the search accuracy, CodeMatcher significantly outperforms three state-of-the-art tools (DeepCS, UNIF, and CodeHow) and two online search engines (GitHub search and Google search).

Discipline

Databases and Information Systems | Programming Languages and Compilers | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ESEC/FSE '22: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Singapore, Singapore, November 14-18

First Page

1642

Last Page

1646

ISBN

9781450394130

Identifier

10.1145/3540250.3558935

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3540250.3558935

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