An Empirical Study of Code Search in Intelligent Coding Assistant: Perceptions, Expectations, and Directions
Abstract
Code search plays an important role in enhancing the productivity of software developers. Throughout the years, numerous code search tools have been developed and widely utilized. Many researchers have conducted empirical studies to understand the practical challenges in using web search engines, like Google and Koders, for code search. To understand the latest industrial practice, we conducted a comprehensive empirical investigation into the code search capability of TONGYI Lingma (short for Lingma), an IDE-based coding assistant recently developed by Alibaba Cloud and available to users worldwide. The investigation involved 146,893 code search events from 24,543 users who consented for recording. The quantitative analysis revealed that developers occasionally perform code search as needed, an effective tool should consistently deliver useful results in practice. To gain deeper insights into developers' perceptions and expectations, we surveyed 53 users and interviewed 7 respondents in person. This study yielded many significant findings, such as developers' expectations for a smarter code search tool capable of understanding their search intents within the local programming context in IDE. Based on the findings, we suggest practical directions for code search researchers and practitioners.
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