An Empirical Study of Code Search in Intelligent Coding Assistant: Perceptions, Expectations, and Directions
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 explore the latest industrial practice, we conducted a comprehensive empirical investigation into the code search capability of 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.
Wed 17 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
11:00 - 12:30 | Code Search and CompletionIndustry Papers / Research Papers at Pitomba Chair(s): Akond Rahman Auburn University | ||
11:00 18mTalk | Leveraging Large Language Models for the Auto-remediation of Microservice Applications - An Experimental Study Industry Papers Komal Sarda York University, Zakeya Namrud York University, Marin Litoiu York University, Canada, Larisa Shwartz IBM T.J. Watson Research, Ian Watts IBM Canada | ||
11:18 18mTalk | CodePlan: Repository-level Coding using LLMs and Planning Research Papers Ramakrishna Bairi Microsoft Research, India, Atharv Sonwane Microsoft Research, India, Aditya Kanade Microsoft Research, India, Vageesh D C Microsoft Research, India, Arun Iyer Microsoft Research, India, Suresh Parthasarathy Microsoft Research, India, Sriram Rajamani Microsoft Research Indua, B. Ashok Microsoft Research. India, Shashank Shet Microsoft Research. India | ||
11:36 18mTalk | An Empirical Study of Code Search in Intelligent Coding Assistant: Perceptions, Expectations, and Directions Industry Papers Chao Liu Chongqing University, Xindong Zhang Alibaba Cloud Computing Co. Ltd., Hongyu Zhang Chongqing University, Zhiyuan Wan Zhejiang University, Zhan Huang Chongqing University, Meng Yan Chongqing University | ||
11:54 18mTalk | DeciX: Explain Deep Learning Based Code Generation Applications Research Papers Simin Chen University of Texas at Dallas, Zexin Li University of California, Riverside, Wei Yang University of Texas at Dallas, Cong Liu University of California, Riverside | ||
12:12 18mTalk | IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion Research Papers Bolun Li Shandong Normal University, Zhihong Sun Shandong Normal University, Tao Huang Shandong Normal University, Hongyu Zhang Chongqing University, Yao Wan Huazhong University of Science and Technology, Chen Lyu Shandong Normal University, Ge Li Peking University, Zhi Jin Peking University | ||