{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:Z3KYIDEPQ4UK6GBAXM7U4NRQDN","short_pith_number":"pith:Z3KYIDEP","schema_version":"1.0","canonical_sha256":"ced5840c8f8728af1820bb3f4e36301b4e279334cceb527d034402ae6e2da9c7","source":{"kind":"arxiv","id":"2005.00972","version":1},"attestation_state":"computed","paper":{"title":"Repairing Deep Neural Networks: Fix Patterns and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Giang Nguyen, Hridesh Rajan, Md Johirul Islam, Rangeet Pan","submitted_at":"2020-05-03T03:06:12Z","abstract_excerpt":"Significant interest in applying Deep Neural Network (DNN) has fueled the need to support engineering of software that uses DNNs. Repairing software that uses DNNs is one such unmistakable SE need where automated tools could be beneficial; however, we do not fully understand challenges to repairing and patterns that are utilized when manually repairing DNNs. What challenges should automated repair tools address? What are the repair patterns whose automation could help developers? Which repair patterns should be assigned a higher priority for building automated bug repair tools? This work prese"},"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":"2005.00972","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2020-05-03T03:06:12Z","cross_cats_sorted":[],"title_canon_sha256":"769efff4af13a99d52bf28024f6beafb43788114abb794e13fa40ead7eeea32b","abstract_canon_sha256":"8772c4540c408355a5c2af506120d05ae7fac50002114e7f627def987509eb34"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:00:02.796821Z","signature_b64":"SdQrN99is7wgDF1qcWpE8GeBuvxfvqprI2OryWz2YQZZ7vPxYOVQW2GVcGCiVhR3bzDaFIobdeh2syEzhAnbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ced5840c8f8728af1820bb3f4e36301b4e279334cceb527d034402ae6e2da9c7","last_reissued_at":"2026-07-05T01:00:02.796382Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:00:02.796382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Repairing Deep Neural Networks: Fix Patterns and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Giang Nguyen, Hridesh Rajan, Md Johirul Islam, Rangeet Pan","submitted_at":"2020-05-03T03:06:12Z","abstract_excerpt":"Significant interest in applying Deep Neural Network (DNN) has fueled the need to support engineering of software that uses DNNs. Repairing software that uses DNNs is one such unmistakable SE need where automated tools could be beneficial; however, we do not fully understand challenges to repairing and patterns that are utilized when manually repairing DNNs. What challenges should automated repair tools address? What are the repair patterns whose automation could help developers? Which repair patterns should be assigned a higher priority for building automated bug repair tools? This work prese"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.00972","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/2005.00972/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":"2005.00972","created_at":"2026-07-05T01:00:02.796438+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.00972v1","created_at":"2026-07-05T01:00:02.796438+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.00972","created_at":"2026-07-05T01:00:02.796438+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z3KYIDEPQ4UK","created_at":"2026-07-05T01:00:02.796438+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z3KYIDEPQ4UK6GBA","created_at":"2026-07-05T01:00:02.796438+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z3KYIDEP","created_at":"2026-07-05T01:00:02.796438+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/Z3KYIDEPQ4UK6GBAXM7U4NRQDN","json":"https://pith.science/pith/Z3KYIDEPQ4UK6GBAXM7U4NRQDN.json","graph_json":"https://pith.science/api/pith-number/Z3KYIDEPQ4UK6GBAXM7U4NRQDN/graph.json","events_json":"https://pith.science/api/pith-number/Z3KYIDEPQ4UK6GBAXM7U4NRQDN/events.json","paper":"https://pith.science/paper/Z3KYIDEP"},"agent_actions":{"view_html":"https://pith.science/pith/Z3KYIDEPQ4UK6GBAXM7U4NRQDN","download_json":"https://pith.science/pith/Z3KYIDEPQ4UK6GBAXM7U4NRQDN.json","view_paper":"https://pith.science/paper/Z3KYIDEP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.00972&json=true","fetch_graph":"https://pith.science/api/pith-number/Z3KYIDEPQ4UK6GBAXM7U4NRQDN/graph.json","fetch_events":"https://pith.science/api/pith-number/Z3KYIDEPQ4UK6GBAXM7U4NRQDN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z3KYIDEPQ4UK6GBAXM7U4NRQDN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z3KYIDEPQ4UK6GBAXM7U4NRQDN/action/storage_attestation","attest_author":"https://pith.science/pith/Z3KYIDEPQ4UK6GBAXM7U4NRQDN/action/author_attestation","sign_citation":"https://pith.science/pith/Z3KYIDEPQ4UK6GBAXM7U4NRQDN/action/citation_signature","submit_replication":"https://pith.science/pith/Z3KYIDEPQ4UK6GBAXM7U4NRQDN/action/replication_record"}},"created_at":"2026-07-05T01:00:02.796438+00:00","updated_at":"2026-07-05T01:00:02.796438+00:00"}