{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:J2J5ZU5MWG2JBHSMN7FQUJZVIA","short_pith_number":"pith:J2J5ZU5M","schema_version":"1.0","canonical_sha256":"4e93dcd3acb1b4909e4c6fcb0a2735402e60e3916a2cd89acec0957420ba9efb","source":{"kind":"arxiv","id":"2507.22610","version":1},"attestation_state":"computed","paper":{"title":"Metamorphic Testing of Deep Code Models: A Systematic Literature Review","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Ali Asgari, Annibale Panichella, Milan De Koning, Pouria Derakhshanfar","submitted_at":"2025-07-30T12:25:30Z","abstract_excerpt":"Large language models and deep learning models designed for code intelligence have revolutionized the software engineering field due to their ability to perform various code-related tasks. These models can process source code and software artifacts with high accuracy in tasks such as code completion, defect detection, and code summarization; therefore, they can potentially become an integral part of modern software engineering practices. Despite these capabilities, robustness remains a critical quality attribute for deep-code models as they may produce different results under varied and advers"},"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":"2507.22610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2025-07-30T12:25:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4d29c1931c2d65c7afea7d914ea90e71ae38cf2383900643f0443b16243926df","abstract_canon_sha256":"62c9d5f8cbffc96162c2c309ccc15edf299d0698821ac14b53237796d2420527"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:45.458820Z","signature_b64":"VsjxFc9BAfOFB+m2Hcf7b4aSNceiiwZdCef6GRuOtnTyVvb2zxo3n6kv8kS6g2V9nMpZPN/NxuRq/8n30ZrZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e93dcd3acb1b4909e4c6fcb0a2735402e60e3916a2cd89acec0957420ba9efb","last_reissued_at":"2026-07-05T11:45:45.458326Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:45.458326Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Metamorphic Testing of Deep Code Models: A Systematic Literature Review","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Ali Asgari, Annibale Panichella, Milan De Koning, Pouria Derakhshanfar","submitted_at":"2025-07-30T12:25:30Z","abstract_excerpt":"Large language models and deep learning models designed for code intelligence have revolutionized the software engineering field due to their ability to perform various code-related tasks. These models can process source code and software artifacts with high accuracy in tasks such as code completion, defect detection, and code summarization; therefore, they can potentially become an integral part of modern software engineering practices. Despite these capabilities, robustness remains a critical quality attribute for deep-code models as they may produce different results under varied and advers"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.22610","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/2507.22610/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":"2507.22610","created_at":"2026-07-05T11:45:45.458379+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.22610v1","created_at":"2026-07-05T11:45:45.458379+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.22610","created_at":"2026-07-05T11:45:45.458379+00:00"},{"alias_kind":"pith_short_12","alias_value":"J2J5ZU5MWG2J","created_at":"2026-07-05T11:45:45.458379+00:00"},{"alias_kind":"pith_short_16","alias_value":"J2J5ZU5MWG2JBHSM","created_at":"2026-07-05T11:45:45.458379+00:00"},{"alias_kind":"pith_short_8","alias_value":"J2J5ZU5M","created_at":"2026-07-05T11:45:45.458379+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17957","citing_title":"Contextualized Code Pretraining for Code Generation","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J2J5ZU5MWG2JBHSMN7FQUJZVIA","json":"https://pith.science/pith/J2J5ZU5MWG2JBHSMN7FQUJZVIA.json","graph_json":"https://pith.science/api/pith-number/J2J5ZU5MWG2JBHSMN7FQUJZVIA/graph.json","events_json":"https://pith.science/api/pith-number/J2J5ZU5MWG2JBHSMN7FQUJZVIA/events.json","paper":"https://pith.science/paper/J2J5ZU5M"},"agent_actions":{"view_html":"https://pith.science/pith/J2J5ZU5MWG2JBHSMN7FQUJZVIA","download_json":"https://pith.science/pith/J2J5ZU5MWG2JBHSMN7FQUJZVIA.json","view_paper":"https://pith.science/paper/J2J5ZU5M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.22610&json=true","fetch_graph":"https://pith.science/api/pith-number/J2J5ZU5MWG2JBHSMN7FQUJZVIA/graph.json","fetch_events":"https://pith.science/api/pith-number/J2J5ZU5MWG2JBHSMN7FQUJZVIA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J2J5ZU5MWG2JBHSMN7FQUJZVIA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J2J5ZU5MWG2JBHSMN7FQUJZVIA/action/storage_attestation","attest_author":"https://pith.science/pith/J2J5ZU5MWG2JBHSMN7FQUJZVIA/action/author_attestation","sign_citation":"https://pith.science/pith/J2J5ZU5MWG2JBHSMN7FQUJZVIA/action/citation_signature","submit_replication":"https://pith.science/pith/J2J5ZU5MWG2JBHSMN7FQUJZVIA/action/replication_record"}},"created_at":"2026-07-05T11:45:45.458379+00:00","updated_at":"2026-07-05T11:45:45.458379+00:00"}