{"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/190403990","title":"Import2vec - Learning Embeddings for Software Libraries","arxiv_id":"1904.03990","date":"2019-03-27","proceeding":null,"authors":["Bart Theeten","Frederik Vandeputte","Tom Van Cutsem"],"abstract":"We consider the problem of developing suitable learning representations\n(embeddings) for library packages that capture semantic similarity among\nlibraries. Such representations are known to improve the performance of\ndownstream learning tasks (e.g. classification) or applications such as\ncontextual search and analogical reasoning.\n  We apply word embedding techniques from natural language processing (NLP) to\ntrain embeddings for library packages (\"library vectors\"). Library vectors\nrepresent libraries by similar context of use as determined by import\nstatements present in source code. Experimental results obtained from training\nsuch embeddings on three large open source software corpora reveals that\nlibrary vectors capture semantically meaningful relationships among software\nlibraries, such as the relationship between frameworks and their plug-ins and\nlibraries commonly used together within ecosystems such as big data\ninfrastructure projects (in Java), front-end and back-end web development\nframeworks (in JavaScript) and data science toolkits (in Python).","url_abs":"http://arxiv.org/abs/1904.03990v1","url_pdf":"http://arxiv.org/pdf/1904.03990v1.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":"190403990","repo_url":"https://github.com/nokia/code-compass","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}