Opportunities and Challenges in Code Search Tools

Author:

Liu Chao1,Xia Xin2,Lo David3,Gao Cuiyun4,Yang Xiaohu1,Grundy John5

Affiliation:

1. Zhejiang University, Hangzhou, Zhejiang, China

2. Huawei, Hangzhou, Zhejiang, China

3. Singapore Management University, Singapore

4. Harbin Institute of Technology (Shenzhen), Shenzhen, China

5. Monash University, Clayton, Victoria, Australia

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.

Funder

National Science Foundation of China

Key Research and Development Program of Zhejiang Province

National Research Foundation, Singapore

ARC Laureate Fellowship

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference152 articles.

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1. VisRepo: A Visual Retrieval Tool for Large-Scale Open-Source Projects;Proceedings of the 15th Asia-Pacific Symposium on Internetware;2024-07-24

2. An Empirical Study on Code Search Pre-trained Models: Academic Progresses vs. Industry Requirements;Proceedings of the 15th Asia-Pacific Symposium on Internetware;2024-07-24

3. Rocks Coding, Not Development: A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks;Proceedings of the ACM on Software Engineering;2024-07-12

4. An Empirical Study of Code Search in Intelligent Coding Assistant: Perceptions, Expectations, and Directions;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10

5. Fusing Code Searchers;IEEE Transactions on Software Engineering;2024-07

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