{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:YTTUS5SBL7GJTXWRMH2WKGASTX","short_pith_number":"pith:YTTUS5SB","schema_version":"1.0","canonical_sha256":"c4e74976415fcc99ded161f56518129dd4572fb524c4fb55fb4cbc6ebcf4044a","source":{"kind":"arxiv","id":"2011.14597","version":1},"attestation_state":"computed","paper":{"title":"A Survey on Deep Learning for Software Engineering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"David Lo, John Grundy, Xin Xia, Yanming Yang","submitted_at":"2020-11-30T07:43:43Z","abstract_excerpt":"In 2006, Geoffrey Hinton proposed the concept of training ''Deep Neural Networks (DNNs)'' and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model opt"},"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":"2011.14597","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2020-11-30T07:43:43Z","cross_cats_sorted":[],"title_canon_sha256":"c3273a9f9a782c770e42900268f614d3389402713a830547d8d419667ebb1d26","abstract_canon_sha256":"ed4b20ef9c73cd4bd03cd303d7cb20f2f9708fc5b216f2a6bbaa210d9806842a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:55:21.587541Z","signature_b64":"9210piPXKseHacSIJaQrCstOvZ+8gPzDbi9XEvsVr5jkipwBZKWVk+cqEKe5DCWDfFz9SOsGmhoIHxmtzLqkBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4e74976415fcc99ded161f56518129dd4572fb524c4fb55fb4cbc6ebcf4044a","last_reissued_at":"2026-07-05T01:55:21.587133Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:55:21.587133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Deep Learning for Software Engineering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"David Lo, John Grundy, Xin Xia, Yanming Yang","submitted_at":"2020-11-30T07:43:43Z","abstract_excerpt":"In 2006, Geoffrey Hinton proposed the concept of training ''Deep Neural Networks (DNNs)'' and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model opt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.14597","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.14597/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":"2011.14597","created_at":"2026-07-05T01:55:21.587189+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.14597v1","created_at":"2026-07-05T01:55:21.587189+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.14597","created_at":"2026-07-05T01:55:21.587189+00:00"},{"alias_kind":"pith_short_12","alias_value":"YTTUS5SBL7GJ","created_at":"2026-07-05T01:55:21.587189+00:00"},{"alias_kind":"pith_short_16","alias_value":"YTTUS5SBL7GJTXWR","created_at":"2026-07-05T01:55:21.587189+00:00"},{"alias_kind":"pith_short_8","alias_value":"YTTUS5SB","created_at":"2026-07-05T01:55:21.587189+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/YTTUS5SBL7GJTXWRMH2WKGASTX","json":"https://pith.science/pith/YTTUS5SBL7GJTXWRMH2WKGASTX.json","graph_json":"https://pith.science/api/pith-number/YTTUS5SBL7GJTXWRMH2WKGASTX/graph.json","events_json":"https://pith.science/api/pith-number/YTTUS5SBL7GJTXWRMH2WKGASTX/events.json","paper":"https://pith.science/paper/YTTUS5SB"},"agent_actions":{"view_html":"https://pith.science/pith/YTTUS5SBL7GJTXWRMH2WKGASTX","download_json":"https://pith.science/pith/YTTUS5SBL7GJTXWRMH2WKGASTX.json","view_paper":"https://pith.science/paper/YTTUS5SB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.14597&json=true","fetch_graph":"https://pith.science/api/pith-number/YTTUS5SBL7GJTXWRMH2WKGASTX/graph.json","fetch_events":"https://pith.science/api/pith-number/YTTUS5SBL7GJTXWRMH2WKGASTX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YTTUS5SBL7GJTXWRMH2WKGASTX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YTTUS5SBL7GJTXWRMH2WKGASTX/action/storage_attestation","attest_author":"https://pith.science/pith/YTTUS5SBL7GJTXWRMH2WKGASTX/action/author_attestation","sign_citation":"https://pith.science/pith/YTTUS5SBL7GJTXWRMH2WKGASTX/action/citation_signature","submit_replication":"https://pith.science/pith/YTTUS5SBL7GJTXWRMH2WKGASTX/action/replication_record"}},"created_at":"2026-07-05T01:55:21.587189+00:00","updated_at":"2026-07-05T01:55:21.587189+00:00"}