{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WUSZI4K6SOSFEU6JFWONOUP7RB","short_pith_number":"pith:WUSZI4K6","schema_version":"1.0","canonical_sha256":"b52594715e93a45253c92d9cd751ff885d68b16532cbdb26327d63e5f9c346c9","source":{"kind":"arxiv","id":"2505.11205","version":3},"attestation_state":"computed","paper":{"title":"IssueCourier: Multi-Relational Heterogeneous Temporal Graph Neural Network for Open-Source Issue Assignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Bing Li, Chunying Zhou, Gong Chen, Peng He, Xiaoyuan Xie","submitted_at":"2025-05-16T13:03:26Z","abstract_excerpt":"Issue assignment plays a critical role in open-source software (OSS) maintenance, which involves recommending the most suitable developers to address the reported issues. Given the high volume of issue reports in large-scale projects, manually assigning issues is tedious and costly. Previous studies have proposed automated issue assignment approaches that primarily focus on modeling issue report textual information, developers' expertise, or interactions between issues and developers based on historical issue-fixing records. However, these approaches often suffer from performance limitations d"},"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":"2505.11205","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-05-16T13:03:26Z","cross_cats_sorted":[],"title_canon_sha256":"65d1b180f66634708b2fd15084a420fe262c7018851bbba54d852f5e1183feff","abstract_canon_sha256":"dcfa2afc7bd7991ba6743d5e3ea1fd89de155c03850a1eda53ab5d1b17cb5896"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:42.944288Z","signature_b64":"RA26N3fitVjlk1qJms6tNgbnVfFPP7yeqyekHDuJv+De0isQ89E4tS4UvH9Q0KBZNBxtX/I1Qv/sKl8LqavzBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b52594715e93a45253c92d9cd751ff885d68b16532cbdb26327d63e5f9c346c9","last_reissued_at":"2026-07-05T11:18:42.943895Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:42.943895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IssueCourier: Multi-Relational Heterogeneous Temporal Graph Neural Network for Open-Source Issue Assignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Bing Li, Chunying Zhou, Gong Chen, Peng He, Xiaoyuan Xie","submitted_at":"2025-05-16T13:03:26Z","abstract_excerpt":"Issue assignment plays a critical role in open-source software (OSS) maintenance, which involves recommending the most suitable developers to address the reported issues. Given the high volume of issue reports in large-scale projects, manually assigning issues is tedious and costly. Previous studies have proposed automated issue assignment approaches that primarily focus on modeling issue report textual information, developers' expertise, or interactions between issues and developers based on historical issue-fixing records. However, these approaches often suffer from performance limitations d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11205","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/2505.11205/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":"2505.11205","created_at":"2026-07-05T11:18:42.943947+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.11205v3","created_at":"2026-07-05T11:18:42.943947+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11205","created_at":"2026-07-05T11:18:42.943947+00:00"},{"alias_kind":"pith_short_12","alias_value":"WUSZI4K6SOSF","created_at":"2026-07-05T11:18:42.943947+00:00"},{"alias_kind":"pith_short_16","alias_value":"WUSZI4K6SOSFEU6J","created_at":"2026-07-05T11:18:42.943947+00:00"},{"alias_kind":"pith_short_8","alias_value":"WUSZI4K6","created_at":"2026-07-05T11:18:42.943947+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.21156","citing_title":"Automated Bug Triaging using Instruction-Tuned Large Language Models","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WUSZI4K6SOSFEU6JFWONOUP7RB","json":"https://pith.science/pith/WUSZI4K6SOSFEU6JFWONOUP7RB.json","graph_json":"https://pith.science/api/pith-number/WUSZI4K6SOSFEU6JFWONOUP7RB/graph.json","events_json":"https://pith.science/api/pith-number/WUSZI4K6SOSFEU6JFWONOUP7RB/events.json","paper":"https://pith.science/paper/WUSZI4K6"},"agent_actions":{"view_html":"https://pith.science/pith/WUSZI4K6SOSFEU6JFWONOUP7RB","download_json":"https://pith.science/pith/WUSZI4K6SOSFEU6JFWONOUP7RB.json","view_paper":"https://pith.science/paper/WUSZI4K6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.11205&json=true","fetch_graph":"https://pith.science/api/pith-number/WUSZI4K6SOSFEU6JFWONOUP7RB/graph.json","fetch_events":"https://pith.science/api/pith-number/WUSZI4K6SOSFEU6JFWONOUP7RB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WUSZI4K6SOSFEU6JFWONOUP7RB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WUSZI4K6SOSFEU6JFWONOUP7RB/action/storage_attestation","attest_author":"https://pith.science/pith/WUSZI4K6SOSFEU6JFWONOUP7RB/action/author_attestation","sign_citation":"https://pith.science/pith/WUSZI4K6SOSFEU6JFWONOUP7RB/action/citation_signature","submit_replication":"https://pith.science/pith/WUSZI4K6SOSFEU6JFWONOUP7RB/action/replication_record"}},"created_at":"2026-07-05T11:18:42.943947+00:00","updated_at":"2026-07-05T11:18:42.943947+00:00"}