{"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/automatic-data-labeling-for-software","title":"Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?","arxiv_id":"2407.17803","date":"2024-07-25","proceeding":null,"authors":["Triet H. M. Le","M. Ali Babar"],"abstract":"Background: Software Vulnerability (SV) prediction needs large-sized and high-quality data to perform well. Current SV datasets mostly require expensive labeling efforts by experts (human-labeled) and thus are limited in size. Meanwhile, there are growing efforts in automatic SV labeling at scale. However, the fitness of auto-labeled data for SV prediction is still largely unknown. Aims: We quantitatively and qualitatively study the quality and use of the state-of-the-art auto-labeled SV data, D2A, for SV prediction. Method: Using multiple sources and manual validation, we curate clean SV data from human-labeled SV-fixing commits in two well-known projects for investigating the auto-labeled counterparts. Results: We discover that 50+% of the auto-labeled SVs are noisy (incorrectly labeled), and they hardly overlap with the publicly reported ones. Yet, SV prediction models utilizing the noisy auto-labeled SVs can perform up to 22% and 90% better in Matthews Correlation Coefficient and Recall, respectively, than the original models. We also reveal the promises and difficulties of applying noise-reduction methods for automatically addressing the noise in auto-labeled SV data to maximize the data utilization for SV prediction. Conclusions: Our study informs the benefits and challenges of using auto-labeled SVs, paving the way for large-scale SV prediction.","url_abs":"https://arxiv.org/abs/2407.17803v1","url_pdf":"https://arxiv.org/pdf/2407.17803v1.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":"automatic-data-labeling-for-software","repo_url":"https://github.com/lhmtriet/autovul","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}