{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RPTFRL7P2ED2XZWICP6QAUBUXC","short_pith_number":"pith:RPTFRL7P","schema_version":"1.0","canonical_sha256":"8be658afefd107abe6c813fd005034b8b6520e777831ef4c3f424ed537a973f0","source":{"kind":"arxiv","id":"2511.15817","version":6},"attestation_state":"computed","paper":{"title":"A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Alejandro Velasco, Daniel Rodriguez-Cardenas, David N. Palacio, Denys Poshyvanyk, Dipin Khati, Luftar Rahman Alif","submitted_at":"2025-11-19T19:18:28Z","abstract_excerpt":"Recent advances in large language models (_LLMs_) have accelerated their adoption in software engineering contexts. However, concerns persist about the structural quality of the code they produce. In particular, _LLMs_ often replicate poor coding practices, introducing code smells (i.e., patterns that hinder readability, maintainability, or design integrity). Although prior research has examined the detection or repair of smells, we still lack a clear understanding of how and when these issues emerge in generated code.\n  This paper addresses this gap by systematically **_measuring_**, **_expla"},"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":"2511.15817","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SE","submitted_at":"2025-11-19T19:18:28Z","cross_cats_sorted":[],"title_canon_sha256":"48376ad0fce609448052c0c576c39fb8cdf20c3cb345f1e6ef2965e86adca763","abstract_canon_sha256":"2d5bb7cd6f810e0427439c2176a938076d1ee91b09a547e142830948fd0c2d51"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T02:09:10.180690Z","signature_b64":"5mI+mWAK9zsNUdO1+8ao9Wf6rVMSRu5ak7tg+pOLmlfKiSTjl8J/VnvwVv9jw2+dGMj+Ek+mIx575p/J2fFrBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8be658afefd107abe6c813fd005034b8b6520e777831ef4c3f424ed537a973f0","last_reissued_at":"2026-08-04T02:09:10.178964Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T02:09:10.178964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Alejandro Velasco, Daniel Rodriguez-Cardenas, David N. Palacio, Denys Poshyvanyk, Dipin Khati, Luftar Rahman Alif","submitted_at":"2025-11-19T19:18:28Z","abstract_excerpt":"Recent advances in large language models (_LLMs_) have accelerated their adoption in software engineering contexts. However, concerns persist about the structural quality of the code they produce. In particular, _LLMs_ often replicate poor coding practices, introducing code smells (i.e., patterns that hinder readability, maintainability, or design integrity). Although prior research has examined the detection or repair of smells, we still lack a clear understanding of how and when these issues emerge in generated code.\n  This paper addresses this gap by systematically **_measuring_**, **_expla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.15817","kind":"arxiv","version":6},"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/2511.15817/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":"2511.15817","created_at":"2026-08-04T02:09:10.180560+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.15817v6","created_at":"2026-08-04T02:09:10.180560+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.15817","created_at":"2026-08-04T02:09:10.180560+00:00"},{"alias_kind":"pith_short_12","alias_value":"RPTFRL7P2ED2","created_at":"2026-08-04T02:09:10.180560+00:00"},{"alias_kind":"pith_short_16","alias_value":"RPTFRL7P2ED2XZWI","created_at":"2026-08-04T02:09:10.180560+00:00"},{"alias_kind":"pith_short_8","alias_value":"RPTFRL7P","created_at":"2026-08-04T02:09:10.180560+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.07001","citing_title":"SmellBench: Evaluating LLM Agents on Architectural Code Smell Repair","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07001","citing_title":"SmellBench: Evaluating LLM Agents on Architectural Code Smell Repair","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RPTFRL7P2ED2XZWICP6QAUBUXC","json":"https://pith.science/pith/RPTFRL7P2ED2XZWICP6QAUBUXC.json","graph_json":"https://pith.science/api/pith-number/RPTFRL7P2ED2XZWICP6QAUBUXC/graph.json","events_json":"https://pith.science/api/pith-number/RPTFRL7P2ED2XZWICP6QAUBUXC/events.json","paper":"https://pith.science/paper/RPTFRL7P"},"agent_actions":{"view_html":"https://pith.science/pith/RPTFRL7P2ED2XZWICP6QAUBUXC","download_json":"https://pith.science/pith/RPTFRL7P2ED2XZWICP6QAUBUXC.json","view_paper":"https://pith.science/paper/RPTFRL7P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.15817&json=true","fetch_graph":"https://pith.science/api/pith-number/RPTFRL7P2ED2XZWICP6QAUBUXC/graph.json","fetch_events":"https://pith.science/api/pith-number/RPTFRL7P2ED2XZWICP6QAUBUXC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RPTFRL7P2ED2XZWICP6QAUBUXC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RPTFRL7P2ED2XZWICP6QAUBUXC/action/storage_attestation","attest_author":"https://pith.science/pith/RPTFRL7P2ED2XZWICP6QAUBUXC/action/author_attestation","sign_citation":"https://pith.science/pith/RPTFRL7P2ED2XZWICP6QAUBUXC/action/citation_signature","submit_replication":"https://pith.science/pith/RPTFRL7P2ED2XZWICP6QAUBUXC/action/replication_record"}},"created_at":"2026-08-04T02:09:10.180560+00:00","updated_at":"2026-08-04T02:09:10.180560+00:00"}