{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T11:52:32Z","timestamp":1791460352688,"version":"4.3.4"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"ISSTA","license":[{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Softw. Eng."],"published-print":{"date-parts":[[2025,6,22]]},"abstract":"<jats:p>\n                    Inspired by advances in deep learning, numerous learning-based approaches for vulnerability detection have emerged, primarily operating at the function level for scalability. However, this design choice has a critical limitation: many vulnerabilities span multiple functions, causing function-level approaches to lose the semantics of called functions and fail to capture true vulnerability patterns. To address this issue, we propose\n                    <jats:italic toggle=\"yes\">VulnSC<\/jats:italic>\n                    , a novel framework designed to enhance learning-based approaches by complementing inter-procedural semantics.\n                    <jats:italic toggle=\"yes\">VulnSC<\/jats:italic>\n                    retrieves the source code of called functions for datasets and leverages large language models (LLMs) with well-designed prompts to generate summaries for these functions. The datasets, enhanced with these summaries, are fed into neural networks for improved vulnerability detection.\n                    <jats:italic toggle=\"yes\">VulnSC<\/jats:italic>\n                    is the first general framework to integrate inter-procedural semantics into existing learning-based approaches for vulnerability detection while maintaining scalability. We evaluate\n                    <jats:italic toggle=\"yes\">VulnSC<\/jats:italic>\n                    on four state-of-theart learning-based approaches using two widely used datasets, and our experimental results demonstrate that\n                    <jats:italic toggle=\"yes\">VulnSC<\/jats:italic>\n                    significantly enhances detection performance with minimal additional computational overhead.\n                  <\/jats:p>","DOI":"10.1145\/3728912","type":"journal-article","created":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T10:52:56Z","timestamp":1750589576000},"page":"825-847","source":"Crossref","is-referenced-by-count":9,"title":["Enhancing Vulnerability Detection via Inter-procedural Semantic Completion"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-2248","authenticated-orcid":false,"given":"Bozhi","family":"Wu","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1296-8046","authenticated-orcid":false,"given":"Chengjie","family":"Liu","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3453-5698","authenticated-orcid":false,"given":"Zhiming","family":"Li","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5694-608X","authenticated-orcid":false,"given":"Yushi","family":"Cao","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3545-1392","authenticated-orcid":false,"given":"Jun","family":"Sun","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9726-3434","authenticated-orcid":false,"given":"Shang-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,22]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2022. checkmarx. https:\/\/www.checkmarx.com"},{"key":"e_1_3_1_3_2","unstructured":"2022. 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