{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ESQI24ZENL7YGGXAFGQSMWPJCG","short_pith_number":"pith:ESQI24ZE","schema_version":"1.0","canonical_sha256":"24a08d73246aff831ae029a12659e91196ded33bd96fe44e1dca2644dc903e19","source":{"kind":"arxiv","id":"1905.03813","version":4},"attestation_state":"computed","paper":{"title":"When Deep Learning Met Code Search","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.SE","authors_text":"Hongyu Li, Jose Cambronero, Koushik Sen, Satish Chandra, Seohyun Kim","submitted_at":"2019-05-09T18:47:38Z","abstract_excerpt":"There have been multiple recent proposals on using deep neural networks for code search using natural language. Common across these proposals is the idea of $\\mathit{embedding}$ code and natural language queries, into real vectors and then using vector distance to approximate semantic correlation between code and the query. Multiple approaches exist for learning these embeddings, including $\\mathit{unsupervised}$ techniques, which rely only on a corpus of code examples, and $\\mathit{supervised}$ techniques, which use an $\\mathit{aligned}$ corpus of paired code and natural language descriptions"},"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":"1905.03813","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2019-05-09T18:47:38Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"05a33ca707ab76f35d1b0bd99907613c10dbf13f6a533ada0b133c686d24843f","abstract_canon_sha256":"8e17b44d40efda74d8a04d473731b8a2221a0ddc5ea89c0f560a6470d32238ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:11:59.578601Z","signature_b64":"LeRHuj8hxSvPzOQ07tJAW3TGY1xttkhiWhRG5XLkPDjH286ScHq3EmFD8nEZJ/Fli9QCDkxiox813l2JcTlTCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24a08d73246aff831ae029a12659e91196ded33bd96fe44e1dca2644dc903e19","last_reissued_at":"2026-07-05T00:11:59.578180Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:11:59.578180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Deep Learning Met Code Search","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.SE","authors_text":"Hongyu Li, Jose Cambronero, Koushik Sen, Satish Chandra, Seohyun Kim","submitted_at":"2019-05-09T18:47:38Z","abstract_excerpt":"There have been multiple recent proposals on using deep neural networks for code search using natural language. Common across these proposals is the idea of $\\mathit{embedding}$ code and natural language queries, into real vectors and then using vector distance to approximate semantic correlation between code and the query. Multiple approaches exist for learning these embeddings, including $\\mathit{unsupervised}$ techniques, which rely only on a corpus of code examples, and $\\mathit{supervised}$ techniques, which use an $\\mathit{aligned}$ corpus of paired code and natural language descriptions"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03813","kind":"arxiv","version":4},"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/1905.03813/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":"1905.03813","created_at":"2026-07-05T00:11:59.578236+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.03813v4","created_at":"2026-07-05T00:11:59.578236+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03813","created_at":"2026-07-05T00:11:59.578236+00:00"},{"alias_kind":"pith_short_12","alias_value":"ESQI24ZENL7Y","created_at":"2026-07-05T00:11:59.578236+00:00"},{"alias_kind":"pith_short_16","alias_value":"ESQI24ZENL7YGGXA","created_at":"2026-07-05T00:11:59.578236+00:00"},{"alias_kind":"pith_short_8","alias_value":"ESQI24ZE","created_at":"2026-07-05T00:11:59.578236+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"1909.09436","citing_title":"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ESQI24ZENL7YGGXAFGQSMWPJCG","json":"https://pith.science/pith/ESQI24ZENL7YGGXAFGQSMWPJCG.json","graph_json":"https://pith.science/api/pith-number/ESQI24ZENL7YGGXAFGQSMWPJCG/graph.json","events_json":"https://pith.science/api/pith-number/ESQI24ZENL7YGGXAFGQSMWPJCG/events.json","paper":"https://pith.science/paper/ESQI24ZE"},"agent_actions":{"view_html":"https://pith.science/pith/ESQI24ZENL7YGGXAFGQSMWPJCG","download_json":"https://pith.science/pith/ESQI24ZENL7YGGXAFGQSMWPJCG.json","view_paper":"https://pith.science/paper/ESQI24ZE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.03813&json=true","fetch_graph":"https://pith.science/api/pith-number/ESQI24ZENL7YGGXAFGQSMWPJCG/graph.json","fetch_events":"https://pith.science/api/pith-number/ESQI24ZENL7YGGXAFGQSMWPJCG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ESQI24ZENL7YGGXAFGQSMWPJCG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ESQI24ZENL7YGGXAFGQSMWPJCG/action/storage_attestation","attest_author":"https://pith.science/pith/ESQI24ZENL7YGGXAFGQSMWPJCG/action/author_attestation","sign_citation":"https://pith.science/pith/ESQI24ZENL7YGGXAFGQSMWPJCG/action/citation_signature","submit_replication":"https://pith.science/pith/ESQI24ZENL7YGGXAFGQSMWPJCG/action/replication_record"}},"created_at":"2026-07-05T00:11:59.578236+00:00","updated_at":"2026-07-05T00:11:59.578236+00:00"}