{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NEGZSS4XL2FURXIJPD3YEDTMFG","short_pith_number":"pith:NEGZSS4X","schema_version":"1.0","canonical_sha256":"690d994b975e8b48dd0978f7820e6c29aa373706c9f754de8b9ae982bcb27e34","source":{"kind":"arxiv","id":"1903.01899","version":3},"attestation_state":"computed","paper":{"title":"A Machine-learning Based Ensemble Method For Anti-patterns Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Antoine Barbez, Foutse Khomh, Yann-Ga\\\"el Gu\\'eh\\'eneuc","submitted_at":"2019-01-29T21:29:05Z","abstract_excerpt":"Anti-patterns are poor solutions to recurring design problems. Several empirical studies have highlighted their negative impact on program comprehension, maintainability, as well as fault-proneness. A variety of detection approaches have been proposed to identify their occurrences in source code. However, these approaches can identify only a subset of the occurrences and report large numbers of false positives and misses. Furthermore, a low agreement is generally observed among different approaches. Recent studies have shown the potential of machine-learning models to improve this situation. H"},"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":"1903.01899","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2019-01-29T21:29:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"29e07df2c80e2410806baa3e48cc7cbc731d673d0b290a3f0932abaacc53e9b4","abstract_canon_sha256":"03967d9a2fb5e9bcff347cf4d9eaaf03e2d8962bf1587446a5834dacd550155f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:12:37.561900Z","signature_b64":"/omlD94SsAnI/dc6dMenFP7s/6EuENtCg0UFlBBmlHeT1TyFDgdENersp/k+To/DJ8YHbEljemSxxLGb4EIDBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"690d994b975e8b48dd0978f7820e6c29aa373706c9f754de8b9ae982bcb27e34","last_reissued_at":"2026-07-05T00:12:37.561484Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:12:37.561484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Machine-learning Based Ensemble Method For Anti-patterns Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Antoine Barbez, Foutse Khomh, Yann-Ga\\\"el Gu\\'eh\\'eneuc","submitted_at":"2019-01-29T21:29:05Z","abstract_excerpt":"Anti-patterns are poor solutions to recurring design problems. Several empirical studies have highlighted their negative impact on program comprehension, maintainability, as well as fault-proneness. A variety of detection approaches have been proposed to identify their occurrences in source code. However, these approaches can identify only a subset of the occurrences and report large numbers of false positives and misses. Furthermore, a low agreement is generally observed among different approaches. Recent studies have shown the potential of machine-learning models to improve this situation. H"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.01899","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/1903.01899/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":"1903.01899","created_at":"2026-07-05T00:12:37.561546+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.01899v3","created_at":"2026-07-05T00:12:37.561546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.01899","created_at":"2026-07-05T00:12:37.561546+00:00"},{"alias_kind":"pith_short_12","alias_value":"NEGZSS4XL2FU","created_at":"2026-07-05T00:12:37.561546+00:00"},{"alias_kind":"pith_short_16","alias_value":"NEGZSS4XL2FURXIJ","created_at":"2026-07-05T00:12:37.561546+00:00"},{"alias_kind":"pith_short_8","alias_value":"NEGZSS4X","created_at":"2026-07-05T00:12:37.561546+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/NEGZSS4XL2FURXIJPD3YEDTMFG","json":"https://pith.science/pith/NEGZSS4XL2FURXIJPD3YEDTMFG.json","graph_json":"https://pith.science/api/pith-number/NEGZSS4XL2FURXIJPD3YEDTMFG/graph.json","events_json":"https://pith.science/api/pith-number/NEGZSS4XL2FURXIJPD3YEDTMFG/events.json","paper":"https://pith.science/paper/NEGZSS4X"},"agent_actions":{"view_html":"https://pith.science/pith/NEGZSS4XL2FURXIJPD3YEDTMFG","download_json":"https://pith.science/pith/NEGZSS4XL2FURXIJPD3YEDTMFG.json","view_paper":"https://pith.science/paper/NEGZSS4X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.01899&json=true","fetch_graph":"https://pith.science/api/pith-number/NEGZSS4XL2FURXIJPD3YEDTMFG/graph.json","fetch_events":"https://pith.science/api/pith-number/NEGZSS4XL2FURXIJPD3YEDTMFG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NEGZSS4XL2FURXIJPD3YEDTMFG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NEGZSS4XL2FURXIJPD3YEDTMFG/action/storage_attestation","attest_author":"https://pith.science/pith/NEGZSS4XL2FURXIJPD3YEDTMFG/action/author_attestation","sign_citation":"https://pith.science/pith/NEGZSS4XL2FURXIJPD3YEDTMFG/action/citation_signature","submit_replication":"https://pith.science/pith/NEGZSS4XL2FURXIJPD3YEDTMFG/action/replication_record"}},"created_at":"2026-07-05T00:12:37.561546+00:00","updated_at":"2026-07-05T00:12:37.561546+00:00"}