{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HS7MQZTJGU7FM5LMPLXMQM7VXF","short_pith_number":"pith:HS7MQZTJ","schema_version":"1.0","canonical_sha256":"3cbec86669353e56756c7aeec833f5b94afe6076e4a1b451d129b6c4536844f2","source":{"kind":"arxiv","id":"2411.06491","version":1},"attestation_state":"computed","paper":{"title":"MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Jiancheng Qian, Jiaxin Chen, Jinliang Ding, Kay Chen Tan, Ke Li","submitted_at":"2024-11-10T15:17:15Z","abstract_excerpt":"Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that effectively use cross-project information and yield satisfactory performance remains challenging. In this paper, we resolve this bottleneck by formulating CPDP as a multi-objective bilevel optimization (MBLO) method, dubbed MBL-CPDP. It comprises two nested problems: the upper-level, a multi-objective combinatorial optimization problem, enhances rob"},"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":"2411.06491","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2024-11-10T15:17:15Z","cross_cats_sorted":[],"title_canon_sha256":"3a057f4b2aec8c14f5da098288dad8467096c32ba6a73c9846f783a47401f93c","abstract_canon_sha256":"b821ae74e49ba328017c07b714dfb15fce018c8449a2b0bc8153b1c614131d8d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:33:47.126392Z","signature_b64":"Ua5pB4kiscgFOKN3YJpOV2E90O0K08ntokdMyR2JCmLwvZnCV9y9Z4yT4EWtTkeNIWOAzDAQ1aYK7+4Qz+AxCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3cbec86669353e56756c7aeec833f5b94afe6076e4a1b451d129b6c4536844f2","last_reissued_at":"2026-07-05T09:33:47.125870Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:33:47.125870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Jiancheng Qian, Jiaxin Chen, Jinliang Ding, Kay Chen Tan, Ke Li","submitted_at":"2024-11-10T15:17:15Z","abstract_excerpt":"Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that effectively use cross-project information and yield satisfactory performance remains challenging. In this paper, we resolve this bottleneck by formulating CPDP as a multi-objective bilevel optimization (MBLO) method, dubbed MBL-CPDP. It comprises two nested problems: the upper-level, a multi-objective combinatorial optimization problem, enhances rob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.06491","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/2411.06491/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":"2411.06491","created_at":"2026-07-05T09:33:47.125931+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.06491v1","created_at":"2026-07-05T09:33:47.125931+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.06491","created_at":"2026-07-05T09:33:47.125931+00:00"},{"alias_kind":"pith_short_12","alias_value":"HS7MQZTJGU7F","created_at":"2026-07-05T09:33:47.125931+00:00"},{"alias_kind":"pith_short_16","alias_value":"HS7MQZTJGU7FM5LM","created_at":"2026-07-05T09:33:47.125931+00:00"},{"alias_kind":"pith_short_8","alias_value":"HS7MQZTJ","created_at":"2026-07-05T09:33:47.125931+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/HS7MQZTJGU7FM5LMPLXMQM7VXF","json":"https://pith.science/pith/HS7MQZTJGU7FM5LMPLXMQM7VXF.json","graph_json":"https://pith.science/api/pith-number/HS7MQZTJGU7FM5LMPLXMQM7VXF/graph.json","events_json":"https://pith.science/api/pith-number/HS7MQZTJGU7FM5LMPLXMQM7VXF/events.json","paper":"https://pith.science/paper/HS7MQZTJ"},"agent_actions":{"view_html":"https://pith.science/pith/HS7MQZTJGU7FM5LMPLXMQM7VXF","download_json":"https://pith.science/pith/HS7MQZTJGU7FM5LMPLXMQM7VXF.json","view_paper":"https://pith.science/paper/HS7MQZTJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.06491&json=true","fetch_graph":"https://pith.science/api/pith-number/HS7MQZTJGU7FM5LMPLXMQM7VXF/graph.json","fetch_events":"https://pith.science/api/pith-number/HS7MQZTJGU7FM5LMPLXMQM7VXF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HS7MQZTJGU7FM5LMPLXMQM7VXF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HS7MQZTJGU7FM5LMPLXMQM7VXF/action/storage_attestation","attest_author":"https://pith.science/pith/HS7MQZTJGU7FM5LMPLXMQM7VXF/action/author_attestation","sign_citation":"https://pith.science/pith/HS7MQZTJGU7FM5LMPLXMQM7VXF/action/citation_signature","submit_replication":"https://pith.science/pith/HS7MQZTJGU7FM5LMPLXMQM7VXF/action/replication_record"}},"created_at":"2026-07-05T09:33:47.125931+00:00","updated_at":"2026-07-05T09:33:47.125931+00:00"}