Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2022
Abstract
Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique used to build code search tools, and classified existing tools into focusing on supporting seven different search tasks. Based on our findings, we identified a set of outstanding challenges in existing studies and a research roadmap for future code search research.
Keywords
Code search, modeling, code retrieval
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ACM Computing Surveys
Volume
54
Issue
9
First Page
1
Last Page
35
ISSN
0360-0300
Identifier
10.1145/3480027
Publisher
Association for Computing Machinery (ACM)
Citation
LIU, Chao; XIA, Xin; LO, David; GAO, Cuiying; YANG, Xiaohu; and GRUNDY, John.
Opportunities and challenges in code search tools. (2022). ACM Computing Surveys. 54, (9), 1-35.
Available at: https://ink.library.smu.edu.sg/sis_research/6926
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3480027