{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:H374L43DTJUHYXPBNLO2WC3UMP","short_pith_number":"pith:H374L43D","canonical_record":{"source":{"id":"2607.23355","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-25T20:28:54Z","cross_cats_sorted":[],"title_canon_sha256":"95772bc60dc0755af20249a4e34cf5f1814d6e98f096f77027d85243f56680db","abstract_canon_sha256":"08fa2138ea3f5903616047b0c35a1a0ebd4d66169535365664893d538b37c2f0"},"schema_version":"1.0"},"canonical_sha256":"3effc5f3639a687c5de16addab0b7463eac6ff5d2a0967e2b9d5b2442a0d3da6","source":{"kind":"arxiv","id":"2607.23355","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.23355","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"arxiv_version","alias_value":"2607.23355v1","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.23355","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"pith_short_12","alias_value":"H374L43DTJUH","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"pith_short_16","alias_value":"H374L43DTJUHYXPB","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"pith_short_8","alias_value":"H374L43D","created_at":"2026-07-28T01:22:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:H374L43DTJUHYXPBNLO2WC3UMP","target":"record","payload":{"canonical_record":{"source":{"id":"2607.23355","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-25T20:28:54Z","cross_cats_sorted":[],"title_canon_sha256":"95772bc60dc0755af20249a4e34cf5f1814d6e98f096f77027d85243f56680db","abstract_canon_sha256":"08fa2138ea3f5903616047b0c35a1a0ebd4d66169535365664893d538b37c2f0"},"schema_version":"1.0"},"canonical_sha256":"3effc5f3639a687c5de16addab0b7463eac6ff5d2a0967e2b9d5b2442a0d3da6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T01:22:48.936364Z","signature_b64":"ecxlR59xhS2KvNwKMgGwTo8B7IBhXJWk+67YkwbEQxF7dw7LqqIwUeUgClOpaNcTklmmPPFGm5nhsJNDCdxpCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3effc5f3639a687c5de16addab0b7463eac6ff5d2a0967e2b9d5b2442a0d3da6","last_reissued_at":"2026-07-28T01:22:48.935542Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T01:22:48.935542Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.23355","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-28T01:22:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YKjNK2hYDK8Il5gpf173URn7ay3MGjVKE0uKCq6xbQjFMb7grPmWueibRCaXIhCGqKxPC93gMcYJVoDxeNtVBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:53:07.130621Z"},"content_sha256":"05c5d77818727b2a28c57ff40091da3224f89f6ebeeeda42ee8047b952359b48","schema_version":"1.0","event_id":"sha256:05c5d77818727b2a28c57ff40091da3224f89f6ebeeeda42ee8047b952359b48"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:H374L43DTJUHYXPBNLO2WC3UMP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Code Understanding for Impact Analysis by Combining Transformers and Program Dependence Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Denys Poshyvanyk, Gabriele Bavota, Kevin Moran, Nathan Cooper, Steve Rich, Yanfu Yan","submitted_at":"2026-07-25T20:28:54Z","abstract_excerpt":"Impact analysis (IA) is a critical software maintenance task that identifies the effects of a given set of code changes on a larger software project with the intention of avoiding potential adverse effects. IA is a cognitively challenging task that involves reasoning about the abstract relationships between various code constructs. Given its difficulty, researchers have worked to automate IA with approaches that primarily use coupling metrics as a measure of the \"connectedness\" of different parts of a software project. Many of these coupling metrics rely on static, dynamic, or evolutionary inf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.23355","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/2607.23355/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-28T01:22:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6w9NO49o52+of3COY7jKE+eAX8lK1qPKr1ylqmC/iEcNDBws0/KgW7+X6fPsKZSyYlFLzsEXzemjn20WyL6dAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:53:07.131074Z"},"content_sha256":"3987cf22235925b1085697bd502b3dd230352da0f4fcea4de38074ce83aa1d6e","schema_version":"1.0","event_id":"sha256:3987cf22235925b1085697bd502b3dd230352da0f4fcea4de38074ce83aa1d6e"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:H374L43DTJUHYXPBNLO2WC3UMP","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/s10664-008-9088-2) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Using information retrieval based coupling measures for impact analysis.Empirical Software Engineering14 (02 2009), 5–32. https://doi.org/10.1007/s10664-008- 9088-2 Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Mic","arxiv_id":"2607.23355","detector":"doi_compliance","evidence":{"ref_index":2009,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1007/s10664-008-","reconstructed_doi":"10.1007/s10664-008-9088-2"},"severity":"advisory","ref_index":2009,"audited_at":"2026-07-31T23:52:19.235612Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/s10664-008-9088-2","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"e63539176326b9b756f362d43393354d6569453d70488aadd96ac212f4648416","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14625,"payload_sha256":"047680283ffde99efe36d42c9833fc4ff98a74869af2808bef94c165d653b5f5","signature_b64":"eHrDhdfA73ah5EuqRwQK+c6MhoaRyYUWycYPcfjLm/LI100I5JOTj5miV0Agph1LlVTMtC8WgEu4I0BwDKyjDA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T23:56:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AVcK17SfJMHM5drFZfMDC7rPDpSN5HWxCJqtfKVmLmibONCpXVNj4e6O1vWcjYBpXUXCbzcAaES/28/wO01rBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:53:07.171816Z"},"content_sha256":"0669a219e6b4b3e51cb6c9a1bef6610d61ab3398b819b728dc3a4e5129d144b9","schema_version":"1.0","event_id":"sha256:0669a219e6b4b3e51cb6c9a1bef6610d61ab3398b819b728dc3a4e5129d144b9"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:H374L43DTJUHYXPBNLO2WC3UMP","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/ICSM.2006.33) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Integrating Influence Mechanisms into Impact Analysis for Increased Precision. In2006 22nd IEEE International Conference on Software Maintenance. 55–65. https://doi.org/10.1109/ICSM.2006. 33 Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie ","arxiv_id":"2607.23355","detector":"doi_compliance","evidence":{"ref_index":2006,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1109/icsm.2006","reconstructed_doi":"10.1109/ICSM.2006.33"},"severity":"advisory","ref_index":2006,"audited_at":"2026-07-31T23:52:19.235612Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/ICSM.2006.33","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"73d2e52998f4af65e101210b98ca2056b1a3ad5f2b486a5862411c762f2dfe88","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14624,"payload_sha256":"202620195ee09375f63f039ff3aedf91f4511f548e737bc8baf3f081a0265caa","signature_b64":"3ZcC6yqTYZubso+Qu5y4hBM/29G2BGwWkAOf77oeUvrcyXIB2mqHPLerqJYc3h7xHjRk3lQakPk7KvWg08juCw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T23:56:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CgJtObRNZuiTGP60JaPoSs3W7CSOMNfkxbMn1rbilv14PrUU7rMtw8oGOm9YfRXPBmq1yQ2T0FxDZH5pEoWeDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:53:07.172152Z"},"content_sha256":"027d66161e4d26c5010fd7c3ab70bd8cb74c4ebc0827d836fd2a3e4892736d26","schema_version":"1.0","event_id":"sha256:027d66161e4d26c5010fd7c3ab70bd8cb74c4ebc0827d836fd2a3e4892736d26"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:H374L43DTJUHYXPBNLO2WC3UMP","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/APSEC.2005.100) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Supporting predictive change impact analysis: a control call graph based technique. In12th Asia-Pacific Software Engineering Conference (APSEC’05). 9 pp.–. https://doi.org/10.1109/APSEC. 2005.100 Markus Borg, Krzysztof Wnuk, Björn Regnell, ","arxiv_id":"2607.23355","detector":"doi_compliance","evidence":{"ref_index":2005,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1109/apsec","reconstructed_doi":"10.1109/APSEC.2005.100"},"severity":"advisory","ref_index":2005,"audited_at":"2026-07-31T23:52:19.235612Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/APSEC.2005.100","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"8ad4d38baca2313843489c092c3c5a06755405e60c81d10c100506e88942118e","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14623,"payload_sha256":"807c9c795445278652c9e86e9631c22f16e958768ca20e589c44daef6a9ea2cf","signature_b64":"Mn9kMFW4BLqdU3N4s1kr7H1XU1aDNI/YAZES4KZ79TU2XEJBWXBG+nEfYvlpGrzw3W6sGFCnxJuDUlVjpXUiDw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T23:56:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tAuCHO5KWBIica9th81WRZhyjMG/RA6GE7/7d/YOOop9P34KtmgmAsgRD3njETHd5r6sGlgDzuHUl19DZEguAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:53:07.172444Z"},"content_sha256":"6b63464081dfcef481b22cd93b7eea3e89d8c464cf7110c4128148b4825ed9ce","schema_version":"1.0","event_id":"sha256:6b63464081dfcef481b22cd93b7eea3e89d8c464cf7110c4128148b4825ed9ce"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:H374L43DTJUHYXPBNLO2WC3UMP","target":"integrity","payload":{"note":"Identifier '10.5555/3157382' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Convolutional neural networks on graphs with fast localized spectral filtering. InProceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain)(NIPS’16). Curran Associates Inc., Red Hook, NY, U","arxiv_id":"2607.23355","detector":"doi_compliance","evidence":{"doi":"10.5555/3157382","arxiv_id":null,"ref_index":17,"raw_excerpt":"Convolutional neural networks on graphs with fast localized spectral filtering. InProceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain)(NIPS’16). Curran Associates Inc., Red Hook, NY, USA, 3844–3852. https://doi.org/10.5555/3157382. 3157527 Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":17,"audited_at":"2026-07-31T23:52:19.235612Z","event_type":"pith.integrity.v1","detected_doi":"10.5555/3157382","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"324134ebb8d89f0ff9d0c3f4b1f4098c42b56e2a8891321445bafe3ef98d911e","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14622,"payload_sha256":"66efc3ca5bfc9dabcdb0438ecbb96964eb82e8bf39a8a81f51f2c67e9d4abad5","signature_b64":"n2wqrn2lCHNm7wkwQEkgDcB3fvtzEHddXaH1F02j1F3gn7MVFhc1Hnl/z5iRxGQrreE/DEEtS6E51W2iOws5DA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T23:56:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0ttbj/YyhWI2u1jascTnxZRa0cf8vJkVsg6NU2/iKzPDZetuLPLSwNg/dW+CN1ALCMFpBghbLJ7efIdQW8vhCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:53:07.172812Z"},"content_sha256":"38631cf208f9a6ed0f25e19e536dc2c3c31383eba2a54db3069d1f0c14d637a6","schema_version":"1.0","event_id":"sha256:38631cf208f9a6ed0f25e19e536dc2c3c31383eba2a54db3069d1f0c14d637a6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H374L43DTJUHYXPBNLO2WC3UMP/bundle.json","state_url":"https://pith.science/pith/H374L43DTJUHYXPBNLO2WC3UMP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H374L43DTJUHYXPBNLO2WC3UMP/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T15:53:07Z","links":{"resolver":"https://pith.science/pith/H374L43DTJUHYXPBNLO2WC3UMP","bundle":"https://pith.science/pith/H374L43DTJUHYXPBNLO2WC3UMP/bundle.json","state":"https://pith.science/pith/H374L43DTJUHYXPBNLO2WC3UMP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H374L43DTJUHYXPBNLO2WC3UMP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:H374L43DTJUHYXPBNLO2WC3UMP","merge_version":"pith-open-graph-merge-v1","event_count":6,"valid_event_count":6,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"08fa2138ea3f5903616047b0c35a1a0ebd4d66169535365664893d538b37c2f0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-25T20:28:54Z","title_canon_sha256":"95772bc60dc0755af20249a4e34cf5f1814d6e98f096f77027d85243f56680db"},"schema_version":"1.0","source":{"id":"2607.23355","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.23355","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"arxiv_version","alias_value":"2607.23355v1","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.23355","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"pith_short_12","alias_value":"H374L43DTJUH","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"pith_short_16","alias_value":"H374L43DTJUHYXPB","created_at":"2026-07-28T01:22:48Z"},{"alias_kind":"pith_short_8","alias_value":"H374L43D","created_at":"2026-07-28T01:22:48Z"}],"graph_snapshots":[{"event_id":"sha256:3987cf22235925b1085697bd502b3dd230352da0f4fcea4de38074ce83aa1d6e","target":"graph","created_at":"2026-07-28T01:22:48Z","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/2607.23355/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Impact analysis (IA) is a critical software maintenance task that identifies the effects of a given set of code changes on a larger software project with the intention of avoiding potential adverse effects. IA is a cognitively challenging task that involves reasoning about the abstract relationships between various code constructs. Given its difficulty, researchers have worked to automate IA with approaches that primarily use coupling metrics as a measure of the \"connectedness\" of different parts of a software project. Many of these coupling metrics rely on static, dynamic, or evolutionary inf","authors_text":"Denys Poshyvanyk, Gabriele Bavota, Kevin Moran, Nathan Cooper, Steve Rich, Yanfu Yan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-25T20:28:54Z","title":"Enhancing Code Understanding for Impact Analysis by Combining Transformers and Program Dependence Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.23355","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":"sha256:05c5d77818727b2a28c57ff40091da3224f89f6ebeeeda42ee8047b952359b48","target":"record","created_at":"2026-07-28T01:22:48Z","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":"08fa2138ea3f5903616047b0c35a1a0ebd4d66169535365664893d538b37c2f0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-25T20:28:54Z","title_canon_sha256":"95772bc60dc0755af20249a4e34cf5f1814d6e98f096f77027d85243f56680db"},"schema_version":"1.0","source":{"id":"2607.23355","kind":"arxiv","version":1}},"canonical_sha256":"3effc5f3639a687c5de16addab0b7463eac6ff5d2a0967e2b9d5b2442a0d3da6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3effc5f3639a687c5de16addab0b7463eac6ff5d2a0967e2b9d5b2442a0d3da6","first_computed_at":"2026-07-28T01:22:48.935542Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-28T01:22:48.935542Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ecxlR59xhS2KvNwKMgGwTo8B7IBhXJWk+67YkwbEQxF7dw7LqqIwUeUgClOpaNcTklmmPPFGm5nhsJNDCdxpCg==","signature_status":"signed_v1","signed_at":"2026-07-28T01:22:48.936364Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.23355","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:027d66161e4d26c5010fd7c3ab70bd8cb74c4ebc0827d836fd2a3e4892736d26","sha256:0669a219e6b4b3e51cb6c9a1bef6610d61ab3398b819b728dc3a4e5129d144b9","sha256:38631cf208f9a6ed0f25e19e536dc2c3c31383eba2a54db3069d1f0c14d637a6","sha256:6b63464081dfcef481b22cd93b7eea3e89d8c464cf7110c4128148b4825ed9ce"]}],"invalid_events":[],"applied_event_ids":["sha256:05c5d77818727b2a28c57ff40091da3224f89f6ebeeeda42ee8047b952359b48","sha256:3987cf22235925b1085697bd502b3dd230352da0f4fcea4de38074ce83aa1d6e"],"state_sha256":"03ea89403c12fd542482d75d430d410e4749a8c4c88841f92c67f7ccdbffcd16"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LmJHbg63YsJ4XCJs89niFv2eT7DM0Aeqj01Vwi7Y9/wlpfBOiwnmwWxrk9b4Uhp+wxGmmW7kyLdx7khEi8/CCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T15:53:07.175006Z","bundle_sha256":"057c5150c47c53b131c2576deece81b506115e54c6ba037f64e09bb63ffb146e"}}