{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7CMDRCTMJGLNIAL434NRX4KPFX","short_pith_number":"pith:7CMDRCTM","schema_version":"1.0","canonical_sha256":"f898388a6c4996d4017cdf1b1bf14f2dd07f0db82612ae931dacf23a76457933","source":{"kind":"arxiv","id":"2309.12499","version":1},"attestation_state":"computed","paper":{"title":"CodePlan: Repository-level Coding using LLMs and Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Aditya Kanade, Arun Iyer, Atharv Sonwane, B. Ashok, Ramakrishna Bairi, Shashank Shet, Sriram Rajamani, Suresh Parthasarathy, Vageesh D C","submitted_at":"2023-09-21T21:45:17Z","abstract_excerpt":"Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as repository-level coding tasks.\n  Recent tools like GitHub Copilot, which are powered by Large Language Models (LLMs), have succeeded in offering high-quality solutions to localized coding problems. Repository-level coding tasks are more involved and cannot be solved directly using LLMs, since code within a repository is inter"},"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":"2309.12499","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-09-21T21:45:17Z","cross_cats_sorted":[],"title_canon_sha256":"4f57ab3eaca626399dd061a2d426719bab25cf0e346584ab689e35b74e4b98f7","abstract_canon_sha256":"765a6340b20f5e4d43ef069ac69e5ad4bd81b794698c0727b2ee710a324694c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:53:13.471483Z","signature_b64":"ecHoDzW7Y2+dtkbUgPz1bU+b2gviHIJd509JBedoOS2Isx5vCtNiO+LTbjOaefcPh8Ok5zS9aOYuQh1Esw2kDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f898388a6c4996d4017cdf1b1bf14f2dd07f0db82612ae931dacf23a76457933","last_reissued_at":"2026-07-05T06:53:13.471078Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:53:13.471078Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CodePlan: Repository-level Coding using LLMs and Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Aditya Kanade, Arun Iyer, Atharv Sonwane, B. Ashok, Ramakrishna Bairi, Shashank Shet, Sriram Rajamani, Suresh Parthasarathy, Vageesh D C","submitted_at":"2023-09-21T21:45:17Z","abstract_excerpt":"Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as repository-level coding tasks.\n  Recent tools like GitHub Copilot, which are powered by Large Language Models (LLMs), have succeeded in offering high-quality solutions to localized coding problems. Repository-level coding tasks are more involved and cannot be solved directly using LLMs, since code within a repository is inter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.12499","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/2309.12499/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":"2309.12499","created_at":"2026-07-05T06:53:13.471135+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.12499v1","created_at":"2026-07-05T06:53:13.471135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.12499","created_at":"2026-07-05T06:53:13.471135+00:00"},{"alias_kind":"pith_short_12","alias_value":"7CMDRCTMJGLN","created_at":"2026-07-05T06:53:13.471135+00:00"},{"alias_kind":"pith_short_16","alias_value":"7CMDRCTMJGLNIAL4","created_at":"2026-07-05T06:53:13.471135+00:00"},{"alias_kind":"pith_short_8","alias_value":"7CMDRCTM","created_at":"2026-07-05T06:53:13.471135+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22417","citing_title":"Code Isn't Memory: A Structural Codebase Index Inside a Coding Agent","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2606.21963","citing_title":"Holmes: Multimodal Agentic Diagnosis for Mixed-Language Mobile Crashes at Industrial Scale","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00953","citing_title":"When Parallelism Pays Off: Cohesion-Aware Task Partitioning for Multi-Agent Coding","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17958","citing_title":"Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2406.00515","citing_title":"A Survey on Large Language Models for Code Generation","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2404.14469","citing_title":"SnapKV: LLM Knows What You are Looking for Before Generation","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2504.15564","citing_title":"OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7CMDRCTMJGLNIAL434NRX4KPFX","json":"https://pith.science/pith/7CMDRCTMJGLNIAL434NRX4KPFX.json","graph_json":"https://pith.science/api/pith-number/7CMDRCTMJGLNIAL434NRX4KPFX/graph.json","events_json":"https://pith.science/api/pith-number/7CMDRCTMJGLNIAL434NRX4KPFX/events.json","paper":"https://pith.science/paper/7CMDRCTM"},"agent_actions":{"view_html":"https://pith.science/pith/7CMDRCTMJGLNIAL434NRX4KPFX","download_json":"https://pith.science/pith/7CMDRCTMJGLNIAL434NRX4KPFX.json","view_paper":"https://pith.science/paper/7CMDRCTM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.12499&json=true","fetch_graph":"https://pith.science/api/pith-number/7CMDRCTMJGLNIAL434NRX4KPFX/graph.json","fetch_events":"https://pith.science/api/pith-number/7CMDRCTMJGLNIAL434NRX4KPFX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7CMDRCTMJGLNIAL434NRX4KPFX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7CMDRCTMJGLNIAL434NRX4KPFX/action/storage_attestation","attest_author":"https://pith.science/pith/7CMDRCTMJGLNIAL434NRX4KPFX/action/author_attestation","sign_citation":"https://pith.science/pith/7CMDRCTMJGLNIAL434NRX4KPFX/action/citation_signature","submit_replication":"https://pith.science/pith/7CMDRCTMJGLNIAL434NRX4KPFX/action/replication_record"}},"created_at":"2026-07-05T06:53:13.471135+00:00","updated_at":"2026-07-05T06:53:13.471135+00:00"}