{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OCPKNKOND3ZZK5EQQYKIT2ORVY","short_pith_number":"pith:OCPKNKON","schema_version":"1.0","canonical_sha256":"709ea6a9cd1ef3957490861489e9d1ae334cb8890797df1ce69428e5ef4fd920","source":{"kind":"arxiv","id":"2402.05102","version":2},"attestation_state":"computed","paper":{"title":"You Can REST Now: Automated REST API Documentation and Testing via LLM-Assisted Request Mutations","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Alix Decrop, Gilles Perrouin, Mike Papadakis, Pierre-Yves Schobbens, Xavier Devroey","submitted_at":"2024-02-07T18:55:41Z","abstract_excerpt":"REST APIs are prevalent among web service implementations, easing interoperability through the HTTP protocol. API testers and users exploit the widely adopted OpenAPI Specification (OAS), a machine-readable standard to document REST APIs. However, documenting APIs is a time-consuming and error-prone task, and existing documentation is not always complete, publicly accessible, or up-to-date. This situation limits the efficiency of testing tools and hinders human comprehension. Large Language Models (LLMs) offer the potential to automatically infer API documentation, using their colossal trainin"},"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":"2402.05102","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2024-02-07T18:55:41Z","cross_cats_sorted":[],"title_canon_sha256":"e7007d43e481f091813989157d0e89a6fb748f2916bd04a2eecf4fb15fb85bf1","abstract_canon_sha256":"1f4a0a75a486fb349581ee6e59d2a61e5afc154c45231a8210b45c37d4a77fbc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:37:41.701562Z","signature_b64":"yJMAKKojPbjXSYCRYT8KuJ3Hz9g5mNVE9iiYnCPQXrSQmg2NG+aAe7HsxNFdT0FAfknnxWF2ebViHYzZtq3HDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"709ea6a9cd1ef3957490861489e9d1ae334cb8890797df1ce69428e5ef4fd920","last_reissued_at":"2026-07-05T11:37:41.701015Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:37:41.701015Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"You Can REST Now: Automated REST API Documentation and Testing via LLM-Assisted Request Mutations","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Alix Decrop, Gilles Perrouin, Mike Papadakis, Pierre-Yves Schobbens, Xavier Devroey","submitted_at":"2024-02-07T18:55:41Z","abstract_excerpt":"REST APIs are prevalent among web service implementations, easing interoperability through the HTTP protocol. API testers and users exploit the widely adopted OpenAPI Specification (OAS), a machine-readable standard to document REST APIs. However, documenting APIs is a time-consuming and error-prone task, and existing documentation is not always complete, publicly accessible, or up-to-date. This situation limits the efficiency of testing tools and hinders human comprehension. Large Language Models (LLMs) offer the potential to automatically infer API documentation, using their colossal trainin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.05102","kind":"arxiv","version":2},"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/2402.05102/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":"2402.05102","created_at":"2026-07-05T11:37:41.701076+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.05102v2","created_at":"2026-07-05T11:37:41.701076+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.05102","created_at":"2026-07-05T11:37:41.701076+00:00"},{"alias_kind":"pith_short_12","alias_value":"OCPKNKOND3ZZ","created_at":"2026-07-05T11:37:41.701076+00:00"},{"alias_kind":"pith_short_16","alias_value":"OCPKNKOND3ZZK5EQ","created_at":"2026-07-05T11:37:41.701076+00:00"},{"alias_kind":"pith_short_8","alias_value":"OCPKNKON","created_at":"2026-07-05T11:37:41.701076+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2409.02977","citing_title":"Large Language Model-Based Agents for Software Engineering: A Survey","ref_index":176,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OCPKNKOND3ZZK5EQQYKIT2ORVY","json":"https://pith.science/pith/OCPKNKOND3ZZK5EQQYKIT2ORVY.json","graph_json":"https://pith.science/api/pith-number/OCPKNKOND3ZZK5EQQYKIT2ORVY/graph.json","events_json":"https://pith.science/api/pith-number/OCPKNKOND3ZZK5EQQYKIT2ORVY/events.json","paper":"https://pith.science/paper/OCPKNKON"},"agent_actions":{"view_html":"https://pith.science/pith/OCPKNKOND3ZZK5EQQYKIT2ORVY","download_json":"https://pith.science/pith/OCPKNKOND3ZZK5EQQYKIT2ORVY.json","view_paper":"https://pith.science/paper/OCPKNKON","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.05102&json=true","fetch_graph":"https://pith.science/api/pith-number/OCPKNKOND3ZZK5EQQYKIT2ORVY/graph.json","fetch_events":"https://pith.science/api/pith-number/OCPKNKOND3ZZK5EQQYKIT2ORVY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OCPKNKOND3ZZK5EQQYKIT2ORVY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OCPKNKOND3ZZK5EQQYKIT2ORVY/action/storage_attestation","attest_author":"https://pith.science/pith/OCPKNKOND3ZZK5EQQYKIT2ORVY/action/author_attestation","sign_citation":"https://pith.science/pith/OCPKNKOND3ZZK5EQQYKIT2ORVY/action/citation_signature","submit_replication":"https://pith.science/pith/OCPKNKOND3ZZK5EQQYKIT2ORVY/action/replication_record"}},"created_at":"2026-07-05T11:37:41.701076+00:00","updated_at":"2026-07-05T11:37:41.701076+00:00"}