{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/evaluating-how-developers-use-general-purpose","title":"Evaluating How Developers Use General-Purpose Web-Search for Code Retrieval","arxiv_id":"1803.08612","date":"2018-03-22","proceeding":null,"authors":["Rahman Md Masudur","Barson Jed","Paul Sydney","Kayan Joshua","Lois Federico Andres","Quezada Sebastian Fernandez","Parnin Christopher","Stolee Kathryn T.","Ray Baishakhi"],"abstract":"Search is an integral part of a software development process. Developers\noften use search engines to look for information during development, including\nreusable code snippets, API understanding, and reference examples. Developers\ntend to prefer general-purpose search engines like Google, which are often not\noptimized for code related documents and use search strategies and ranking\ntechniques that are more optimized for generic, non-code related information.\nIn this paper, we explore whether a general purpose search engine like Google\nis an optimal choice for code-related searches. In particular, we investigate\nwhether the performance of searching with Google varies for code vs. non-code\nrelated searches. To analyze this, we collect search logs from 310 developers\nthat contains nearly 150,000 search queries from Google and the associated\nresult clicks. To differentiate between code-related searches and non-code\nrelated searches, we build a model which identifies the code intent of queries.\nLeveraging this model, we build an automatic classifier that detects a code and\nnon-code related query. We confirm the effectiveness of the classifier on\nmanually annotated queries where the classifier achieves a precision of 87%, a\nrecall of 86%, and an F1-score of 87%. We apply this classifier to\nautomatically annotate all the queries in the dataset. Analyzing this dataset,\nwe observe that code related searching often requires more effort (e.g., time,\nresult clicks, and query modifications) than general non-code search, which\nindicates code search performance with a general search engine is less\neffective.","url_abs":"http://arxiv.org/abs/1803.08612v1","url_pdf":"http://arxiv.org/pdf/1803.08612v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"evaluating-how-developers-use-general-purpose","repo_url":"https://github.com/masud99r/code-intent","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"code-search","task_name":"Code Search"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}