{"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/adapting-neural-text-classification-for","title":"Adapting Neural Text Classification for Improved Software Categorization","arxiv_id":"1806.01742","date":"2018-06-05","proceeding":null,"authors":["Alexander LeClair","Zachary Eberhart","Collin McMillan"],"abstract":"Software Categorization is the task of organizing software into groups that\nbroadly describe the behavior of the software, such as \"editors\" or \"science.\"\nCategorization plays an important role in several maintenance tasks, such as\nrepository navigation and feature elicitation. Current approaches attempt to\ncast the problem as text classification, to make use of the rich body of\nliterature from the NLP domain. However, as we will show in this paper, text\nclassification algorithms are generally not applicable off-the-shelf to source\ncode; we found that they work well when high-level project descriptions are\navailable, but suffer very large performance penalties when classifying source\ncode and comments only. We propose a set of adaptations to a state-of-the-art\nneural classification algorithm and perform two evaluations: one with reference\ndata from Debian end-user programs, and one with a set of C/C++ libraries that\nwe hired professional programmers to annotate. We show that our proposed\napproach achieves performance exceeding that of previous software\nclassification techniques as well as a state-of-the-art neural text\nclassification technique.","url_abs":"http://arxiv.org/abs/1806.01742v2","url_pdf":"http://arxiv.org/pdf/1806.01742v2.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":"adapting-neural-text-classification-for","repo_url":"https://github.com/paqs2020/paqs2020","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}