10 Years Later: Revisiting How Developers Search for Code
Kathryn T. Stolee, Tobias Welp, Caitlin Sadowski, Sebastian G. Elbaum
Abstract
Code search is an integral part of a developer’s workflow. In 2015, researchers published a paper reflecting on the code search practices at Google of 27 developers who used the internal Code Search tool. That paper had first-hand accounts for why those developers were using code search and highlighted how often and in what situations developers were searching for code. In the past decade, much has changed in the landscape of developer support. New languages have emerged, artificial intelligence (AI) for code generation has gained traction, auto-complete in the IDE has gotten better, Q&A forums have increased in popularity, and code repositories are larger than ever. It is worth considering whether those observations from almost a decade ago have stood the test of time. In this work, inspired by the prior survey about the Code Search tool, we run a series of three surveys with 1,945 total responses and report overall Code Search usage statistics for over 100,000 users. Unlike the prior work, in our surveys, we include explicit success criteria to understand when code search is meeting their needs, and when it is not. We dive further into two common sub-categories of code search effort: when its users are looking for examples and when they are using code search alongside code review. We find that Code Search users continue to use the tool frequently and the frequency has not changed despite the introduction of AI-enhanced development support. Users continue to turn to Code Search to find examples, but the frequency of example-seeking behavior has decreased. More often than before, users access the tool to learn about and explore code. This has implications for future Code Search support in software development.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers1
Ask how each one uses itBuilds on4
- Using an LLM to Help With Code UnderstandingDaye Nam, Andrew Macvean, Vincent J. Hellendoorn, Bogdan Vasilescu et al.ICSE 2024 · 264 citations
- Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical StudyQi Guo, Junming Cao, Xiaofei Xie, Shangqing Liu et al.ICSE 2024 · 107 citations
- Code Search is All You Need? Improving Code Suggestions with Code SearchJunkai Chen, Xing Hu, Zhenhao Li, Cuiyun Gao et al.ICSE 2024 · 31 citations
- Two Birds with One Stone: Boosting Code Generation and Code Search via a Generative Adversarial NetworkShangwen Wang, Bo Lin, Zhensu Sun, Ming Wen et al.OOPSLA 2023 · 21 citations
Related papers
- "My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding AssistantsYunbo Lyu, Zhou Yang, Jieke Shi, Jianming Chang et al.ASE 2025 · 8 citations
- A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and ChallengesJenny T. Liang, Chenyang Yang, Brad A. MyersICSE 2024 · 126 citations
- The Effect of Google Search on Software Security: Unobtrusive Security Interventions via Content Re-rankingFelix Fischer, Yannick Stachelscheid, Jens GrossklagsCCS 2021 · 12 citations
- CodaMosa: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language ModelsCaroline Lemieux, Jeevana Priya Inala, Shuvendu K. Lahiri, Siddhartha SenICSE 2023 · 221 citations
- NBSearch: Semantic Search and Visual Exploration of Computational NotebooksXingjun Li, Yuanxin Wang, Hong Wang, Yang Wang et al.CHI 2021 · 22 citations
