{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UVGVPYO2T5FIQ5G4KCPIGBRM4W","short_pith_number":"pith:UVGVPYO2","schema_version":"1.0","canonical_sha256":"a54d57e1da9f4a8874dc509e83062ce590043395acbd0ffae660d34b7b697915","source":{"kind":"arxiv","id":"2507.01477","version":1},"attestation_state":"computed","paper":{"title":"Combining Type Inference and Automated Unit Test Generation for Python","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Gordon Fraser, Lukas Krodinger, Stephan Lukasczyk","submitted_at":"2025-07-02T08:41:28Z","abstract_excerpt":"Automated unit test generation is an established research field that has so far focused on statically-typed programming languages. The lack of type information in dynamically-typed programming languages, such as Python, inhibits test generators, which heavily rely on information about parameter and return types of functions to select suitable arguments when constructing test cases. Since automated test generators inherently rely on frequent execution of candidate tests, we make use of these frequent executions to address this problem by introducing type tracing, which extracts type-related inf"},"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":"2507.01477","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-07-02T08:41:28Z","cross_cats_sorted":[],"title_canon_sha256":"5ff6ca1911b584fbdaa920d7a0e19d4f9ed2b7f0b42309f30183f316f46c6703","abstract_canon_sha256":"51c1a3d24b695b94d2540d85e5971cb0468d04e5adacd86f57d64f0ae446aef9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:30:49.590836Z","signature_b64":"pZm2PzCWBPBV1L5XTixsnAvMtQXyJfr+jdGPPtWvZDKzI1ZoRW77FA5mkjcJD9ZReCsQCHpgxRI/cjzyJ8gGDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a54d57e1da9f4a8874dc509e83062ce590043395acbd0ffae660d34b7b697915","last_reissued_at":"2026-07-05T11:30:49.590264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:30:49.590264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Combining Type Inference and Automated Unit Test Generation for Python","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Gordon Fraser, Lukas Krodinger, Stephan Lukasczyk","submitted_at":"2025-07-02T08:41:28Z","abstract_excerpt":"Automated unit test generation is an established research field that has so far focused on statically-typed programming languages. The lack of type information in dynamically-typed programming languages, such as Python, inhibits test generators, which heavily rely on information about parameter and return types of functions to select suitable arguments when constructing test cases. Since automated test generators inherently rely on frequent execution of candidate tests, we make use of these frequent executions to address this problem by introducing type tracing, which extracts type-related inf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.01477","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/2507.01477/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":"2507.01477","created_at":"2026-07-05T11:30:49.590321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.01477v1","created_at":"2026-07-05T11:30:49.590321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.01477","created_at":"2026-07-05T11:30:49.590321+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVGVPYO2T5FI","created_at":"2026-07-05T11:30:49.590321+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVGVPYO2T5FIQ5G4","created_at":"2026-07-05T11:30:49.590321+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVGVPYO2","created_at":"2026-07-05T11:30:49.590321+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/UVGVPYO2T5FIQ5G4KCPIGBRM4W","json":"https://pith.science/pith/UVGVPYO2T5FIQ5G4KCPIGBRM4W.json","graph_json":"https://pith.science/api/pith-number/UVGVPYO2T5FIQ5G4KCPIGBRM4W/graph.json","events_json":"https://pith.science/api/pith-number/UVGVPYO2T5FIQ5G4KCPIGBRM4W/events.json","paper":"https://pith.science/paper/UVGVPYO2"},"agent_actions":{"view_html":"https://pith.science/pith/UVGVPYO2T5FIQ5G4KCPIGBRM4W","download_json":"https://pith.science/pith/UVGVPYO2T5FIQ5G4KCPIGBRM4W.json","view_paper":"https://pith.science/paper/UVGVPYO2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.01477&json=true","fetch_graph":"https://pith.science/api/pith-number/UVGVPYO2T5FIQ5G4KCPIGBRM4W/graph.json","fetch_events":"https://pith.science/api/pith-number/UVGVPYO2T5FIQ5G4KCPIGBRM4W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVGVPYO2T5FIQ5G4KCPIGBRM4W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVGVPYO2T5FIQ5G4KCPIGBRM4W/action/storage_attestation","attest_author":"https://pith.science/pith/UVGVPYO2T5FIQ5G4KCPIGBRM4W/action/author_attestation","sign_citation":"https://pith.science/pith/UVGVPYO2T5FIQ5G4KCPIGBRM4W/action/citation_signature","submit_replication":"https://pith.science/pith/UVGVPYO2T5FIQ5G4KCPIGBRM4W/action/replication_record"}},"created_at":"2026-07-05T11:30:49.590321+00:00","updated_at":"2026-07-05T11:30:49.590321+00:00"}