{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CZFCW5JKPUA6I75SKOOFIZ2TOU","short_pith_number":"pith:CZFCW5JK","schema_version":"1.0","canonical_sha256":"164a2b752a7d01e47fb2539c546753750451f28838a6c3baef63fe7602684a68","source":{"kind":"arxiv","id":"2505.18216","version":1},"attestation_state":"computed","paper":{"title":"Data Mining-Based Techniques for Software Fault Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"LACODAM), Mireille Ducass\\'e (DRUID), Olivier Ridoux (DRUID), Peggy Cellier (INSA Rennes, S\\'ebastien Ferr\\'e (LACODAM), W. Eric Wong","submitted_at":"2025-05-23T07:35:10Z","abstract_excerpt":"This chapter illustrates the basic concepts of fault localization using a data mining technique. It utilizes the Trityp program to illustrate the general method. Formal concept analysis and association rule are two well-known methods for symbolic data mining. In their original inception, they both consider data in the form of an object-attribute table. In their original inception, they both consider data in the form of an object-attribute table. The chapter considers a debugging process in which a program is tested against different test cases. Two attributes, PASS and FAIL, represent the issu"},"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":"2505.18216","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-05-23T07:35:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cc632bca8b84af00cd6c7f14185416d74739264cac8125da7d248729cbed476f","abstract_canon_sha256":"b20d87dcfbc4cd8d6ac726471e9a927bdbdc406087bb55370109dd1311ce3d29"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:43.037163Z","signature_b64":"o7rI7/i8+hyTUDl28fsE0Jl1tCkXhEXkta7+qYiawIMiFrdDkVo7T3ljjgIixJIiTCTsfzYxkb6JKlshQ/nVCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"164a2b752a7d01e47fb2539c546753750451f28838a6c3baef63fe7602684a68","last_reissued_at":"2026-07-05T11:08:43.036702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:43.036702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data Mining-Based Techniques for Software Fault Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"LACODAM), Mireille Ducass\\'e (DRUID), Olivier Ridoux (DRUID), Peggy Cellier (INSA Rennes, S\\'ebastien Ferr\\'e (LACODAM), W. Eric Wong","submitted_at":"2025-05-23T07:35:10Z","abstract_excerpt":"This chapter illustrates the basic concepts of fault localization using a data mining technique. It utilizes the Trityp program to illustrate the general method. Formal concept analysis and association rule are two well-known methods for symbolic data mining. In their original inception, they both consider data in the form of an object-attribute table. In their original inception, they both consider data in the form of an object-attribute table. The chapter considers a debugging process in which a program is tested against different test cases. Two attributes, PASS and FAIL, represent the issu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18216","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/2505.18216/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":"2505.18216","created_at":"2026-07-05T11:08:43.036760+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.18216v1","created_at":"2026-07-05T11:08:43.036760+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18216","created_at":"2026-07-05T11:08:43.036760+00:00"},{"alias_kind":"pith_short_12","alias_value":"CZFCW5JKPUA6","created_at":"2026-07-05T11:08:43.036760+00:00"},{"alias_kind":"pith_short_16","alias_value":"CZFCW5JKPUA6I75S","created_at":"2026-07-05T11:08:43.036760+00:00"},{"alias_kind":"pith_short_8","alias_value":"CZFCW5JK","created_at":"2026-07-05T11:08:43.036760+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/CZFCW5JKPUA6I75SKOOFIZ2TOU","json":"https://pith.science/pith/CZFCW5JKPUA6I75SKOOFIZ2TOU.json","graph_json":"https://pith.science/api/pith-number/CZFCW5JKPUA6I75SKOOFIZ2TOU/graph.json","events_json":"https://pith.science/api/pith-number/CZFCW5JKPUA6I75SKOOFIZ2TOU/events.json","paper":"https://pith.science/paper/CZFCW5JK"},"agent_actions":{"view_html":"https://pith.science/pith/CZFCW5JKPUA6I75SKOOFIZ2TOU","download_json":"https://pith.science/pith/CZFCW5JKPUA6I75SKOOFIZ2TOU.json","view_paper":"https://pith.science/paper/CZFCW5JK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.18216&json=true","fetch_graph":"https://pith.science/api/pith-number/CZFCW5JKPUA6I75SKOOFIZ2TOU/graph.json","fetch_events":"https://pith.science/api/pith-number/CZFCW5JKPUA6I75SKOOFIZ2TOU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CZFCW5JKPUA6I75SKOOFIZ2TOU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CZFCW5JKPUA6I75SKOOFIZ2TOU/action/storage_attestation","attest_author":"https://pith.science/pith/CZFCW5JKPUA6I75SKOOFIZ2TOU/action/author_attestation","sign_citation":"https://pith.science/pith/CZFCW5JKPUA6I75SKOOFIZ2TOU/action/citation_signature","submit_replication":"https://pith.science/pith/CZFCW5JKPUA6I75SKOOFIZ2TOU/action/replication_record"}},"created_at":"2026-07-05T11:08:43.036760+00:00","updated_at":"2026-07-05T11:08:43.036760+00:00"}