{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7ZHAAM4H3V6V4XJQ23DZ53UMGW","short_pith_number":"pith:7ZHAAM4H","schema_version":"1.0","canonical_sha256":"fe4e003387dd7d5e5d30d6c79eee8c35aeb8dbc4e73322e4e6b14f113ee36b77","source":{"kind":"arxiv","id":"2412.18531","version":2},"attestation_state":"computed","paper":{"title":"Automated Code Review In Practice","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Arda \\.I\\c{c}\\\"oz, Baykal Mehmet U\\c{c}ar, Emircan Furkan Bayendur, Eray T\\\"uz\\\"un, Mert Kaan G\\\"ul, \\\"Omercan Devran, Umut Cihan, Vahid Haratian","submitted_at":"2024-12-24T16:24:45Z","abstract_excerpt":"Code review is a widespread practice to improve software quality and transfer knowledge. It is often seen as time-consuming due to the need for manual effort and potential delays. Several AI-assisted tools, such as Qodo, GitHub Copilot, and Coderabbit, provide automated reviews using large language models (LLMs). The effects of such tools in the industry are yet to be examined.\n  This study examines the impact of LLM-based automated code review tools in an industrial setting. The study was conducted within a software development environment that adopted an AI-assisted review tool (based on ope"},"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":"2412.18531","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-12-24T16:24:45Z","cross_cats_sorted":[],"title_canon_sha256":"f5df43abb756264bd560c26d68d65e436e1496ac4b333a920b7db8bc285099d3","abstract_canon_sha256":"839887fe43d6954c438b1cc96c1c5916306af907c3c03163a554214687ddfe86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:49.645825Z","signature_b64":"FV135kaODPR80qXCYQKygsz9nflLIlPcc6zBXV/HLWSmzWBbAf3QzTowIuiq6Xhv+i2cv+BJOGCmzy2bSknIBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe4e003387dd7d5e5d30d6c79eee8c35aeb8dbc4e73322e4e6b14f113ee36b77","last_reissued_at":"2026-07-05T09:54:49.645313Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:49.645313Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automated Code Review In Practice","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Arda \\.I\\c{c}\\\"oz, Baykal Mehmet U\\c{c}ar, Emircan Furkan Bayendur, Eray T\\\"uz\\\"un, Mert Kaan G\\\"ul, \\\"Omercan Devran, Umut Cihan, Vahid Haratian","submitted_at":"2024-12-24T16:24:45Z","abstract_excerpt":"Code review is a widespread practice to improve software quality and transfer knowledge. It is often seen as time-consuming due to the need for manual effort and potential delays. Several AI-assisted tools, such as Qodo, GitHub Copilot, and Coderabbit, provide automated reviews using large language models (LLMs). The effects of such tools in the industry are yet to be examined.\n  This study examines the impact of LLM-based automated code review tools in an industrial setting. The study was conducted within a software development environment that adopted an AI-assisted review tool (based on ope"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.18531","kind":"arxiv","version":2},"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/2412.18531/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":"2412.18531","created_at":"2026-07-05T09:54:49.645384+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.18531v2","created_at":"2026-07-05T09:54:49.645384+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.18531","created_at":"2026-07-05T09:54:49.645384+00:00"},{"alias_kind":"pith_short_12","alias_value":"7ZHAAM4H3V6V","created_at":"2026-07-05T09:54:49.645384+00:00"},{"alias_kind":"pith_short_16","alias_value":"7ZHAAM4H3V6V4XJQ","created_at":"2026-07-05T09:54:49.645384+00:00"},{"alias_kind":"pith_short_8","alias_value":"7ZHAAM4H","created_at":"2026-07-05T09:54:49.645384+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.22534","citing_title":"Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2508.18771","citing_title":"Does AI Code Review Lead to Code Changes? A Case Study of GitHub Actions","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2507.15003","citing_title":"The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03196","citing_title":"From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7ZHAAM4H3V6V4XJQ23DZ53UMGW","json":"https://pith.science/pith/7ZHAAM4H3V6V4XJQ23DZ53UMGW.json","graph_json":"https://pith.science/api/pith-number/7ZHAAM4H3V6V4XJQ23DZ53UMGW/graph.json","events_json":"https://pith.science/api/pith-number/7ZHAAM4H3V6V4XJQ23DZ53UMGW/events.json","paper":"https://pith.science/paper/7ZHAAM4H"},"agent_actions":{"view_html":"https://pith.science/pith/7ZHAAM4H3V6V4XJQ23DZ53UMGW","download_json":"https://pith.science/pith/7ZHAAM4H3V6V4XJQ23DZ53UMGW.json","view_paper":"https://pith.science/paper/7ZHAAM4H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.18531&json=true","fetch_graph":"https://pith.science/api/pith-number/7ZHAAM4H3V6V4XJQ23DZ53UMGW/graph.json","fetch_events":"https://pith.science/api/pith-number/7ZHAAM4H3V6V4XJQ23DZ53UMGW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7ZHAAM4H3V6V4XJQ23DZ53UMGW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7ZHAAM4H3V6V4XJQ23DZ53UMGW/action/storage_attestation","attest_author":"https://pith.science/pith/7ZHAAM4H3V6V4XJQ23DZ53UMGW/action/author_attestation","sign_citation":"https://pith.science/pith/7ZHAAM4H3V6V4XJQ23DZ53UMGW/action/citation_signature","submit_replication":"https://pith.science/pith/7ZHAAM4H3V6V4XJQ23DZ53UMGW/action/replication_record"}},"created_at":"2026-07-05T09:54:49.645384+00:00","updated_at":"2026-07-05T09:54:49.645384+00:00"}