{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FGKFOXSS2TSIGJVS3Q2Q7EC2OW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7e42847e9072efd42eccc70446f88e6643f4dac6685536deb212033aa47727f6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2022-01-18T09:58:33Z","title_canon_sha256":"e192e4f18e9ef08233a0ac327c6ed6150c8c289e246ac3bf4fa5b5f5c3ff31bd"},"schema_version":"1.0","source":{"id":"2201.06850","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.06850","created_at":"2026-07-05T03:49:31Z"},{"alias_kind":"arxiv_version","alias_value":"2201.06850v1","created_at":"2026-07-05T03:49:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.06850","created_at":"2026-07-05T03:49:31Z"},{"alias_kind":"pith_short_12","alias_value":"FGKFOXSS2TSI","created_at":"2026-07-05T03:49:31Z"},{"alias_kind":"pith_short_16","alias_value":"FGKFOXSS2TSIGJVS","created_at":"2026-07-05T03:49:31Z"},{"alias_kind":"pith_short_8","alias_value":"FGKFOXSS","created_at":"2026-07-05T03:49:31Z"}],"graph_snapshots":[{"event_id":"sha256:ac75e73d560adef601dd92d6ac0ed35849d881a7e48dcc37821df783592c2a1a","target":"graph","created_at":"2026-07-05T03:49:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2201.06850/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility of automating specific code review tasks. We recently proposed Deep Learning (DL) models targeting the automation of two tasks: the first model takes as input a code submitted for review and implements in it changes likely to be recommended by a reviewer; the second takes as input the submitted code and a reviewer comment posted in natural language and automatically implements the change required by the reviewer. Whi","authors_text":"Antonio Mastropaolo, Denys Poshyvanyk, Gabriele Bavota, Luca Pascarella, Rosalia Tufano, Simone Masiero","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2022-01-18T09:58:33Z","title":"Using Pre-Trained Models to Boost Code Review Automation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.06850","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:62505801da7ff5242c9d5b4783ec8a96502ff47b8239eabed2466263ed40e2fb","target":"record","created_at":"2026-07-05T03:49:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7e42847e9072efd42eccc70446f88e6643f4dac6685536deb212033aa47727f6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2022-01-18T09:58:33Z","title_canon_sha256":"e192e4f18e9ef08233a0ac327c6ed6150c8c289e246ac3bf4fa5b5f5c3ff31bd"},"schema_version":"1.0","source":{"id":"2201.06850","kind":"arxiv","version":1}},"canonical_sha256":"2994575e52d4e48326b2dc350f905a7590c6cc931a484c792cd8ecb6569e8bed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2994575e52d4e48326b2dc350f905a7590c6cc931a484c792cd8ecb6569e8bed","first_computed_at":"2026-07-05T03:49:31.790132Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:49:31.790132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kap9s80NLQjtNGxkE/qfiumas7npjq0kPTx1JFm1XX7I7/27O6hJfTP1KhFh+7mot1zgXHWVJ/WnYycBFNeTDg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:49:31.790533Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.06850","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:62505801da7ff5242c9d5b4783ec8a96502ff47b8239eabed2466263ed40e2fb","sha256:ac75e73d560adef601dd92d6ac0ed35849d881a7e48dcc37821df783592c2a1a"],"state_sha256":"34e5237308b18634bfbd580eb0a7f7466d741d9927c4a840b933fbc9bf707589"}