Opportunities and challenges in code search tools

• Main Authors: LIU, Chao, XIA, Xin, LO, David, GAO, Cuiying, YANG, Xiaohu, GRUNDY, John
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Code search; modeling; code retrieval; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6926; https://ink.library.smu.edu.sg/context/sis_research/article/7929/viewcontent/Opp_Challenges_Code_Search_Tools_av.pdf
• Institution: Singapore Management University

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.