{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LVWNOD3DHFIW6A5QUOCAPTXHET","short_pith_number":"pith:LVWNOD3D","schema_version":"1.0","canonical_sha256":"5d6cd70f6339516f03b0a38407cee724d0def8ec005b981156bb79ca1e03956c","source":{"kind":"arxiv","id":"2406.05514","version":3},"attestation_state":"computed","paper":{"title":"RAG-Enhanced Commit Message Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Chong Wang, Hongyi Zhang, Linghao Zhang, Peng Liang","submitted_at":"2024-06-08T16:24:24Z","abstract_excerpt":"Commit message is one of the most important textual information in software development and maintenance. However, it is time-consuming to write commit messages manually. Commit Message Generation (CMG) has become a research hotspot. Recently, several pre-trained language models (PLMs) and large language models (LLMs) with code capabilities have been introduced, demonstrating impressive performance on code-related tasks. Meanwhile, prior studies have explored the utilization of retrieval techniques for CMG, but it is still unclear what effects would emerge from combining advanced retrieval tech"},"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":"2406.05514","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-06-08T16:24:24Z","cross_cats_sorted":[],"title_canon_sha256":"2844d3066e0efb47db9de4c330dff6dc71adfd606b908f8bdaae26f6a28a0737","abstract_canon_sha256":"7353fc40bbea4770bd7ff5bfbfbd13371edd6efad0e127585575fb4d869ad2d6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:11.400714Z","signature_b64":"TC9mPrqnGYZWDMtq9Gb7VXImQ5PZpcldHOVEQ4oQcKMCAud9wZWKuHmJZeLQKdxWW7sDG968HnZMrZS1JigCBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d6cd70f6339516f03b0a38407cee724d0def8ec005b981156bb79ca1e03956c","last_reissued_at":"2026-07-05T09:15:11.400274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:11.400274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RAG-Enhanced Commit Message Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Chong Wang, Hongyi Zhang, Linghao Zhang, Peng Liang","submitted_at":"2024-06-08T16:24:24Z","abstract_excerpt":"Commit message is one of the most important textual information in software development and maintenance. However, it is time-consuming to write commit messages manually. Commit Message Generation (CMG) has become a research hotspot. Recently, several pre-trained language models (PLMs) and large language models (LLMs) with code capabilities have been introduced, demonstrating impressive performance on code-related tasks. Meanwhile, prior studies have explored the utilization of retrieval techniques for CMG, but it is still unclear what effects would emerge from combining advanced retrieval tech"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05514","kind":"arxiv","version":3},"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/2406.05514/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":"2406.05514","created_at":"2026-07-05T09:15:11.400340+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05514v3","created_at":"2026-07-05T09:15:11.400340+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05514","created_at":"2026-07-05T09:15:11.400340+00:00"},{"alias_kind":"pith_short_12","alias_value":"LVWNOD3DHFIW","created_at":"2026-07-05T09:15:11.400340+00:00"},{"alias_kind":"pith_short_16","alias_value":"LVWNOD3DHFIW6A5Q","created_at":"2026-07-05T09:15:11.400340+00:00"},{"alias_kind":"pith_short_8","alias_value":"LVWNOD3D","created_at":"2026-07-05T09:15:11.400340+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02256","citing_title":"CommitSuite: A Comprehensive Benchmark for Commit Classification and Message Generation","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LVWNOD3DHFIW6A5QUOCAPTXHET","json":"https://pith.science/pith/LVWNOD3DHFIW6A5QUOCAPTXHET.json","graph_json":"https://pith.science/api/pith-number/LVWNOD3DHFIW6A5QUOCAPTXHET/graph.json","events_json":"https://pith.science/api/pith-number/LVWNOD3DHFIW6A5QUOCAPTXHET/events.json","paper":"https://pith.science/paper/LVWNOD3D"},"agent_actions":{"view_html":"https://pith.science/pith/LVWNOD3DHFIW6A5QUOCAPTXHET","download_json":"https://pith.science/pith/LVWNOD3DHFIW6A5QUOCAPTXHET.json","view_paper":"https://pith.science/paper/LVWNOD3D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05514&json=true","fetch_graph":"https://pith.science/api/pith-number/LVWNOD3DHFIW6A5QUOCAPTXHET/graph.json","fetch_events":"https://pith.science/api/pith-number/LVWNOD3DHFIW6A5QUOCAPTXHET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LVWNOD3DHFIW6A5QUOCAPTXHET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LVWNOD3DHFIW6A5QUOCAPTXHET/action/storage_attestation","attest_author":"https://pith.science/pith/LVWNOD3DHFIW6A5QUOCAPTXHET/action/author_attestation","sign_citation":"https://pith.science/pith/LVWNOD3DHFIW6A5QUOCAPTXHET/action/citation_signature","submit_replication":"https://pith.science/pith/LVWNOD3DHFIW6A5QUOCAPTXHET/action/replication_record"}},"created_at":"2026-07-05T09:15:11.400340+00:00","updated_at":"2026-07-05T09:15:11.400340+00:00"}