{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ZNC5CSDQKS5GXLYSFGV2F2Y5H4","short_pith_number":"pith:ZNC5CSDQ","schema_version":"1.0","canonical_sha256":"cb45d1487054ba6baf1229aba2eb1d3f32b5cf77f3da189c38f74d49bf3373d4","source":{"kind":"arxiv","id":"2111.04666","version":1},"attestation_state":"computed","paper":{"title":"Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Alessio Gambi, Bill Bosshard, Christian Birchler, Sajad Khatiri, Sebastiano Panichella","submitted_at":"2021-11-08T17:34:29Z","abstract_excerpt":"Abstract Simulation platforms facilitate the development of emerging cyber-physical systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational tests. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test scenarios. Past results on software testing optimization have shown that not all the tests contribute equally to establishing confidence in test subjects' quality and reliability, with some \\uninformative\" tests that can be skipped (or remov"},"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":"2111.04666","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2021-11-08T17:34:29Z","cross_cats_sorted":[],"title_canon_sha256":"5b41dd021c8b246e179d10fa4b546dfd05e67fc952f74d692199744514e48f82","abstract_canon_sha256":"a58be1c88c0f51d0cb6eb1ea983f8601a7acc8f40fb7408524307222c7ae1ce4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:30:02.987705Z","signature_b64":"MIFjz13O9Pq9mBAtgr/R2ksb83vcuXRWmBLrFcowBZb28RK9ARy/LZnf1Rt6T52DyBBtr7on3dJBbyBz9VIxCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb45d1487054ba6baf1229aba2eb1d3f32b5cf77f3da189c38f74d49bf3373d4","last_reissued_at":"2026-07-05T03:30:02.987159Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:30:02.987159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Alessio Gambi, Bill Bosshard, Christian Birchler, Sajad Khatiri, Sebastiano Panichella","submitted_at":"2021-11-08T17:34:29Z","abstract_excerpt":"Abstract Simulation platforms facilitate the development of emerging cyber-physical systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational tests. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test scenarios. Past results on software testing optimization have shown that not all the tests contribute equally to establishing confidence in test subjects' quality and reliability, with some \\uninformative\" tests that can be skipped (or remov"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.04666","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/2111.04666/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":"2111.04666","created_at":"2026-07-05T03:30:02.987240+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.04666v1","created_at":"2026-07-05T03:30:02.987240+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.04666","created_at":"2026-07-05T03:30:02.987240+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZNC5CSDQKS5G","created_at":"2026-07-05T03:30:02.987240+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZNC5CSDQKS5GXLYS","created_at":"2026-07-05T03:30:02.987240+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZNC5CSDQ","created_at":"2026-07-05T03:30:02.987240+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/ZNC5CSDQKS5GXLYSFGV2F2Y5H4","json":"https://pith.science/pith/ZNC5CSDQKS5GXLYSFGV2F2Y5H4.json","graph_json":"https://pith.science/api/pith-number/ZNC5CSDQKS5GXLYSFGV2F2Y5H4/graph.json","events_json":"https://pith.science/api/pith-number/ZNC5CSDQKS5GXLYSFGV2F2Y5H4/events.json","paper":"https://pith.science/paper/ZNC5CSDQ"},"agent_actions":{"view_html":"https://pith.science/pith/ZNC5CSDQKS5GXLYSFGV2F2Y5H4","download_json":"https://pith.science/pith/ZNC5CSDQKS5GXLYSFGV2F2Y5H4.json","view_paper":"https://pith.science/paper/ZNC5CSDQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.04666&json=true","fetch_graph":"https://pith.science/api/pith-number/ZNC5CSDQKS5GXLYSFGV2F2Y5H4/graph.json","fetch_events":"https://pith.science/api/pith-number/ZNC5CSDQKS5GXLYSFGV2F2Y5H4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZNC5CSDQKS5GXLYSFGV2F2Y5H4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZNC5CSDQKS5GXLYSFGV2F2Y5H4/action/storage_attestation","attest_author":"https://pith.science/pith/ZNC5CSDQKS5GXLYSFGV2F2Y5H4/action/author_attestation","sign_citation":"https://pith.science/pith/ZNC5CSDQKS5GXLYSFGV2F2Y5H4/action/citation_signature","submit_replication":"https://pith.science/pith/ZNC5CSDQKS5GXLYSFGV2F2Y5H4/action/replication_record"}},"created_at":"2026-07-05T03:30:02.987240+00:00","updated_at":"2026-07-05T03:30:02.987240+00:00"}