{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AOF6LBDT67WZ2R4KN3WP2INTLO","short_pith_number":"pith:AOF6LBDT","schema_version":"1.0","canonical_sha256":"038be58473f7ed9d478a6eecfd21b35b84020683c19a4df10f3f5cff65d84b44","source":{"kind":"arxiv","id":"2204.06643","version":1},"attestation_state":"computed","paper":{"title":"Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.PL","cs.SE"],"primary_cat":"cs.LG","authors_text":"Lee Pike, Qiang Zhou, Xingjian Shi, Yaojie Hu","submitted_at":"2022-04-13T21:39:01Z","abstract_excerpt":"We introduce NSEdit (neural-symbolic edit), a novel Transformer-based code repair method. Given only the source code that contains bugs, NSEdit predicts an editing sequence that can fix the bugs. The edit grammar is formulated as a regular language, and the Transformer uses it as a neural-symbolic scripting interface to generate editing programs. We modify the Transformer and add a pointer network to select the edit locations. An ensemble of rerankers are trained to re-rank the editing sequences generated by beam search. We fine-tune the rerankers on the validation set to reduce over-fitting. "},"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":"2204.06643","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-13T21:39:01Z","cross_cats_sorted":["cs.AI","cs.PL","cs.SE"],"title_canon_sha256":"a8c765126e6fed8c79e1315ff19370ae07fcffcf63d412f14ca87210f01addd1","abstract_canon_sha256":"f6410104105321d5f6cb68178b3c6b5b219697732ad3b25ea2db31e35941f341"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:15:16.039088Z","signature_b64":"UAsb3ZEFoyhUSIy5t7x44f7s3ks5frlMAat+BCg4DUrs/McvBdZCKaSy7Z5QbdNOih2selGl2wtSPVDNjx+nDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"038be58473f7ed9d478a6eecfd21b35b84020683c19a4df10f3f5cff65d84b44","last_reissued_at":"2026-07-05T04:15:16.038637Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:15:16.038637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.PL","cs.SE"],"primary_cat":"cs.LG","authors_text":"Lee Pike, Qiang Zhou, Xingjian Shi, Yaojie Hu","submitted_at":"2022-04-13T21:39:01Z","abstract_excerpt":"We introduce NSEdit (neural-symbolic edit), a novel Transformer-based code repair method. Given only the source code that contains bugs, NSEdit predicts an editing sequence that can fix the bugs. The edit grammar is formulated as a regular language, and the Transformer uses it as a neural-symbolic scripting interface to generate editing programs. We modify the Transformer and add a pointer network to select the edit locations. An ensemble of rerankers are trained to re-rank the editing sequences generated by beam search. We fine-tune the rerankers on the validation set to reduce over-fitting. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.06643","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/2204.06643/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":"2204.06643","created_at":"2026-07-05T04:15:16.038713+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.06643v1","created_at":"2026-07-05T04:15:16.038713+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.06643","created_at":"2026-07-05T04:15:16.038713+00:00"},{"alias_kind":"pith_short_12","alias_value":"AOF6LBDT67WZ","created_at":"2026-07-05T04:15:16.038713+00:00"},{"alias_kind":"pith_short_16","alias_value":"AOF6LBDT67WZ2R4K","created_at":"2026-07-05T04:15:16.038713+00:00"},{"alias_kind":"pith_short_8","alias_value":"AOF6LBDT","created_at":"2026-07-05T04:15:16.038713+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/AOF6LBDT67WZ2R4KN3WP2INTLO","json":"https://pith.science/pith/AOF6LBDT67WZ2R4KN3WP2INTLO.json","graph_json":"https://pith.science/api/pith-number/AOF6LBDT67WZ2R4KN3WP2INTLO/graph.json","events_json":"https://pith.science/api/pith-number/AOF6LBDT67WZ2R4KN3WP2INTLO/events.json","paper":"https://pith.science/paper/AOF6LBDT"},"agent_actions":{"view_html":"https://pith.science/pith/AOF6LBDT67WZ2R4KN3WP2INTLO","download_json":"https://pith.science/pith/AOF6LBDT67WZ2R4KN3WP2INTLO.json","view_paper":"https://pith.science/paper/AOF6LBDT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.06643&json=true","fetch_graph":"https://pith.science/api/pith-number/AOF6LBDT67WZ2R4KN3WP2INTLO/graph.json","fetch_events":"https://pith.science/api/pith-number/AOF6LBDT67WZ2R4KN3WP2INTLO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AOF6LBDT67WZ2R4KN3WP2INTLO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AOF6LBDT67WZ2R4KN3WP2INTLO/action/storage_attestation","attest_author":"https://pith.science/pith/AOF6LBDT67WZ2R4KN3WP2INTLO/action/author_attestation","sign_citation":"https://pith.science/pith/AOF6LBDT67WZ2R4KN3WP2INTLO/action/citation_signature","submit_replication":"https://pith.science/pith/AOF6LBDT67WZ2R4KN3WP2INTLO/action/replication_record"}},"created_at":"2026-07-05T04:15:16.038713+00:00","updated_at":"2026-07-05T04:15:16.038713+00:00"}