{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:L2QPOM632RJVSGRJYTZFYZAVET","short_pith_number":"pith:L2QPOM63","schema_version":"1.0","canonical_sha256":"5ea0f733dbd453591a29c4f25c641524c0dfd497c517fc3c4e7e0869788c69de","source":{"kind":"arxiv","id":"2112.10893","version":2},"attestation_state":"computed","paper":{"title":"VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Alessandro Morari, Baishakhi Ray, Gail Kaiser, Jim Laredo, Sahil Suneja, Yangruibo Ding, Yunhui Zheng","submitted_at":"2021-12-20T22:45:27Z","abstract_excerpt":"Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in today's software ecosystem, where vulnerable code can flow easily and unwittingly within and across software repositories like GitHub. Across such millions of lines of code, traditional static and dynamic approaches struggle to scale. Although existing machine-learning-based approaches look promising in such a setting, most work detects vulnerable code at a higher granularity -- at the method or file level. Thus, dev"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2112.10893","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2021-12-20T22:45:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0765c36e562736aabd593d908cc88d1487cbf45dcdcca2627809a54cd60440ef","abstract_canon_sha256":"13b524af20d56946c4fe6f05a080554fcbbb1289368819da27691a4131c638ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:48:10.067313Z","signature_b64":"tvbdl+zUyEtbFj+S5Ho3QDvWZMYFFLJHmwAcsNrY6wValbwmYghbUnDuBpKsQPd93+RLdi/t4EqDNoGzWlizAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ea0f733dbd453591a29c4f25c641524c0dfd497c517fc3c4e7e0869788c69de","last_reissued_at":"2026-07-05T03:48:10.066878Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:48:10.066878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Alessandro Morari, Baishakhi Ray, Gail Kaiser, Jim Laredo, Sahil Suneja, Yangruibo Ding, Yunhui Zheng","submitted_at":"2021-12-20T22:45:27Z","abstract_excerpt":"Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in today's software ecosystem, where vulnerable code can flow easily and unwittingly within and across software repositories like GitHub. Across such millions of lines of code, traditional static and dynamic approaches struggle to scale. Although existing machine-learning-based approaches look promising in such a setting, most work detects vulnerable code at a higher granularity -- at the method or file level. Thus, dev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.10893","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2112.10893/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2112.10893","created_at":"2026-07-05T03:48:10.066936+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.10893v2","created_at":"2026-07-05T03:48:10.066936+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.10893","created_at":"2026-07-05T03:48:10.066936+00:00"},{"alias_kind":"pith_short_12","alias_value":"L2QPOM632RJV","created_at":"2026-07-05T03:48:10.066936+00:00"},{"alias_kind":"pith_short_16","alias_value":"L2QPOM632RJVSGRJ","created_at":"2026-07-05T03:48:10.066936+00:00"},{"alias_kind":"pith_short_8","alias_value":"L2QPOM63","created_at":"2026-07-05T03:48:10.066936+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L2QPOM632RJVSGRJYTZFYZAVET","json":"https://pith.science/pith/L2QPOM632RJVSGRJYTZFYZAVET.json","graph_json":"https://pith.science/api/pith-number/L2QPOM632RJVSGRJYTZFYZAVET/graph.json","events_json":"https://pith.science/api/pith-number/L2QPOM632RJVSGRJYTZFYZAVET/events.json","paper":"https://pith.science/paper/L2QPOM63"},"agent_actions":{"view_html":"https://pith.science/pith/L2QPOM632RJVSGRJYTZFYZAVET","download_json":"https://pith.science/pith/L2QPOM632RJVSGRJYTZFYZAVET.json","view_paper":"https://pith.science/paper/L2QPOM63","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.10893&json=true","fetch_graph":"https://pith.science/api/pith-number/L2QPOM632RJVSGRJYTZFYZAVET/graph.json","fetch_events":"https://pith.science/api/pith-number/L2QPOM632RJVSGRJYTZFYZAVET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L2QPOM632RJVSGRJYTZFYZAVET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L2QPOM632RJVSGRJYTZFYZAVET/action/storage_attestation","attest_author":"https://pith.science/pith/L2QPOM632RJVSGRJYTZFYZAVET/action/author_attestation","sign_citation":"https://pith.science/pith/L2QPOM632RJVSGRJYTZFYZAVET/action/citation_signature","submit_replication":"https://pith.science/pith/L2QPOM632RJVSGRJYTZFYZAVET/action/replication_record"}},"created_at":"2026-07-05T03:48:10.066936+00:00","updated_at":"2026-07-05T03:48:10.066936+00:00"}