{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:675ZYTJ3JYKXMBIH2WYA53J45Y","short_pith_number":"pith:675ZYTJ3","schema_version":"1.0","canonical_sha256":"f7fb9c4d3b4e15760507d5b00eed3cee102730d433fdfe3957efe8e25713b3c0","source":{"kind":"arxiv","id":"1608.02715","version":1},"attestation_state":"computed","paper":{"title":"A deep language model for software code","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.SE","authors_text":"Hoa Khanh Dam, Trang Pham, Truyen Tran","submitted_at":"2016-08-09T08:16:42Z","abstract_excerpt":"Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term dependencies which occur frequently in software code. Results from our intrinsic evaluation on a corpus of Java projects have demonstrated the effectiveness of "},"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":"1608.02715","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2016-08-09T08:16:42Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ddcf2d171fce837c037272127048469417ec01b0112e04e7f99eab42f233aedf","abstract_canon_sha256":"76ce4157d3c261cb6eb874efbb480543b0dcc45ac51d7983101edf616bd48fc2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:09:33.863364Z","signature_b64":"fF88qu1j0OgXzkfVHHvj5+ay6npWMBqDvbZGN3Xd0Q1mX7+2Hxhhvoj3R40pt/JTbHO8JzWLXGs3zkmbV7uRAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7fb9c4d3b4e15760507d5b00eed3cee102730d433fdfe3957efe8e25713b3c0","last_reissued_at":"2026-05-18T01:09:33.862920Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:09:33.862920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A deep language model for software code","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.SE","authors_text":"Hoa Khanh Dam, Trang Pham, Truyen Tran","submitted_at":"2016-08-09T08:16:42Z","abstract_excerpt":"Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term dependencies which occur frequently in software code. Results from our intrinsic evaluation on a corpus of Java projects have demonstrated the effectiveness of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.02715","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1608.02715","created_at":"2026-05-18T01:09:33.862986+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.02715v1","created_at":"2026-05-18T01:09:33.862986+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.02715","created_at":"2026-05-18T01:09:33.862986+00:00"},{"alias_kind":"pith_short_12","alias_value":"675ZYTJ3JYKX","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_16","alias_value":"675ZYTJ3JYKXMBIH","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_8","alias_value":"675ZYTJ3","created_at":"2026-05-18T12:30:01.593930+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/675ZYTJ3JYKXMBIH2WYA53J45Y","json":"https://pith.science/pith/675ZYTJ3JYKXMBIH2WYA53J45Y.json","graph_json":"https://pith.science/api/pith-number/675ZYTJ3JYKXMBIH2WYA53J45Y/graph.json","events_json":"https://pith.science/api/pith-number/675ZYTJ3JYKXMBIH2WYA53J45Y/events.json","paper":"https://pith.science/paper/675ZYTJ3"},"agent_actions":{"view_html":"https://pith.science/pith/675ZYTJ3JYKXMBIH2WYA53J45Y","download_json":"https://pith.science/pith/675ZYTJ3JYKXMBIH2WYA53J45Y.json","view_paper":"https://pith.science/paper/675ZYTJ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.02715&json=true","fetch_graph":"https://pith.science/api/pith-number/675ZYTJ3JYKXMBIH2WYA53J45Y/graph.json","fetch_events":"https://pith.science/api/pith-number/675ZYTJ3JYKXMBIH2WYA53J45Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/675ZYTJ3JYKXMBIH2WYA53J45Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/675ZYTJ3JYKXMBIH2WYA53J45Y/action/storage_attestation","attest_author":"https://pith.science/pith/675ZYTJ3JYKXMBIH2WYA53J45Y/action/author_attestation","sign_citation":"https://pith.science/pith/675ZYTJ3JYKXMBIH2WYA53J45Y/action/citation_signature","submit_replication":"https://pith.science/pith/675ZYTJ3JYKXMBIH2WYA53J45Y/action/replication_record"}},"created_at":"2026-05-18T01:09:33.862986+00:00","updated_at":"2026-05-18T01:09:33.862986+00:00"}