{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NSPPHUHQPKHTSUAP7VRNUBPVWM","short_pith_number":"pith:NSPPHUHQ","schema_version":"1.0","canonical_sha256":"6c9ef3d0f07a8f39500ffd62da05f5b3280e89da35ceff5c2983f780b8a247ad","source":{"kind":"arxiv","id":"2407.02824","version":1},"attestation_state":"computed","paper":{"title":"Exploring the Capabilities of LLMs for Code Change Related Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"David Lo, Jiakun Liu, Lishui Fan, Shanping Li, Xin Xia, Zhongxin Liu","submitted_at":"2024-07-03T05:49:18Z","abstract_excerpt":"Developers deal with code-change-related tasks daily, e.g., reviewing code. Pre-trained code and code-change-oriented models have been adapted to help developers with such tasks. Recently, large language models (LLMs) have shown their effectiveness in code-related tasks. However, existing LLMs for code focus on general code syntax and semantics rather than the differences between two code versions. Thus, it is an open question how LLMs perform on code-change-related tasks.\n  To answer this question, we conduct an empirical study using \\textgreater 1B parameters LLMs on three code-change-relate"},"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":"2407.02824","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-07-03T05:49:18Z","cross_cats_sorted":[],"title_canon_sha256":"19073d9019afa1fa9cac2877e27230c0082a14bf4a5c002081a20da42fb655b6","abstract_canon_sha256":"4dce7c70367d3cb3a8d7a13fe2bd4708f88d9c7778d7c6d68d893a50a7524b75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:39:42.273639Z","signature_b64":"d+yMLTrSz2YKeru5NidwBOEJHKz/89fFMyIc3S8KeY0Npe0WfXcOlZSBU6dnZcB+2dJfLsbctVIOnq67nYNmDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c9ef3d0f07a8f39500ffd62da05f5b3280e89da35ceff5c2983f780b8a247ad","last_reissued_at":"2026-07-05T08:39:42.273247Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:39:42.273247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring the Capabilities of LLMs for Code Change Related Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"David Lo, Jiakun Liu, Lishui Fan, Shanping Li, Xin Xia, Zhongxin Liu","submitted_at":"2024-07-03T05:49:18Z","abstract_excerpt":"Developers deal with code-change-related tasks daily, e.g., reviewing code. Pre-trained code and code-change-oriented models have been adapted to help developers with such tasks. Recently, large language models (LLMs) have shown their effectiveness in code-related tasks. However, existing LLMs for code focus on general code syntax and semantics rather than the differences between two code versions. Thus, it is an open question how LLMs perform on code-change-related tasks.\n  To answer this question, we conduct an empirical study using \\textgreater 1B parameters LLMs on three code-change-relate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02824","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/2407.02824/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":"2407.02824","created_at":"2026-07-05T08:39:42.273304+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.02824v1","created_at":"2026-07-05T08:39:42.273304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02824","created_at":"2026-07-05T08:39:42.273304+00:00"},{"alias_kind":"pith_short_12","alias_value":"NSPPHUHQPKHT","created_at":"2026-07-05T08:39:42.273304+00:00"},{"alias_kind":"pith_short_16","alias_value":"NSPPHUHQPKHTSUAP","created_at":"2026-07-05T08:39:42.273304+00:00"},{"alias_kind":"pith_short_8","alias_value":"NSPPHUHQ","created_at":"2026-07-05T08:39:42.273304+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.10330","citing_title":"Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NSPPHUHQPKHTSUAP7VRNUBPVWM","json":"https://pith.science/pith/NSPPHUHQPKHTSUAP7VRNUBPVWM.json","graph_json":"https://pith.science/api/pith-number/NSPPHUHQPKHTSUAP7VRNUBPVWM/graph.json","events_json":"https://pith.science/api/pith-number/NSPPHUHQPKHTSUAP7VRNUBPVWM/events.json","paper":"https://pith.science/paper/NSPPHUHQ"},"agent_actions":{"view_html":"https://pith.science/pith/NSPPHUHQPKHTSUAP7VRNUBPVWM","download_json":"https://pith.science/pith/NSPPHUHQPKHTSUAP7VRNUBPVWM.json","view_paper":"https://pith.science/paper/NSPPHUHQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.02824&json=true","fetch_graph":"https://pith.science/api/pith-number/NSPPHUHQPKHTSUAP7VRNUBPVWM/graph.json","fetch_events":"https://pith.science/api/pith-number/NSPPHUHQPKHTSUAP7VRNUBPVWM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NSPPHUHQPKHTSUAP7VRNUBPVWM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NSPPHUHQPKHTSUAP7VRNUBPVWM/action/storage_attestation","attest_author":"https://pith.science/pith/NSPPHUHQPKHTSUAP7VRNUBPVWM/action/author_attestation","sign_citation":"https://pith.science/pith/NSPPHUHQPKHTSUAP7VRNUBPVWM/action/citation_signature","submit_replication":"https://pith.science/pith/NSPPHUHQPKHTSUAP7VRNUBPVWM/action/replication_record"}},"created_at":"2026-07-05T08:39:42.273304+00:00","updated_at":"2026-07-05T08:39:42.273304+00:00"}