{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:FAT2EM4HVPZFBI5COKU4T4RFOW","short_pith_number":"pith:FAT2EM4H","schema_version":"1.0","canonical_sha256":"2827a23387abf250a3a272a9c9f22575b49f41c76191ec4e05c81e5033d6e263","source":{"kind":"arxiv","id":"1511.01874","version":2},"attestation_state":"computed","paper":{"title":"Abstraction Refinement Guided by a Learnt Probabilistic Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.PL","authors_text":"Hongseok Yang, Radu Grigore","submitted_at":"2015-11-05T19:57:03Z","abstract_excerpt":"The core challenge in designing an effective static program analysis is to find a good program abstraction -- one that retains only details relevant to a given query. In this paper, we present a new approach for automatically finding such an abstraction. Our approach uses a pessimistic strategy, which can optionally use guidance from a probabilistic model. Our approach applies to parametric static analyses implemented in Datalog, and is based on counterexample-guided abstraction refinement. For each untried abstraction, our probabilistic model provides a probability of success, while the size "},"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":"1511.01874","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2015-11-05T19:57:03Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"9525514eb0c46c7f7be6b4c9d0e83b6db74f8510f5ad2f89ced4e98be051f8bb","abstract_canon_sha256":"128004f44be265e343e2d8cb9f25cbefd93c2574391a539e663a142ae70eeb91"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:27:21.888901Z","signature_b64":"sjMibU2H/E7nMV5djBQDLcFIg7kjuLUr+wlhXOo9g4vLz9Tjc0fwvinlppDpvKeHYR1ImyVz9S7sAP2hWllrCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2827a23387abf250a3a272a9c9f22575b49f41c76191ec4e05c81e5033d6e263","last_reissued_at":"2026-05-18T01:27:21.888455Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:27:21.888455Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Abstraction Refinement Guided by a Learnt Probabilistic Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.PL","authors_text":"Hongseok Yang, Radu Grigore","submitted_at":"2015-11-05T19:57:03Z","abstract_excerpt":"The core challenge in designing an effective static program analysis is to find a good program abstraction -- one that retains only details relevant to a given query. In this paper, we present a new approach for automatically finding such an abstraction. Our approach uses a pessimistic strategy, which can optionally use guidance from a probabilistic model. Our approach applies to parametric static analyses implemented in Datalog, and is based on counterexample-guided abstraction refinement. For each untried abstraction, our probabilistic model provides a probability of success, while the size "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.01874","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":""},"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":"1511.01874","created_at":"2026-05-18T01:27:21.888521+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.01874v2","created_at":"2026-05-18T01:27:21.888521+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.01874","created_at":"2026-05-18T01:27:21.888521+00:00"},{"alias_kind":"pith_short_12","alias_value":"FAT2EM4HVPZF","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_16","alias_value":"FAT2EM4HVPZFBI5C","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_8","alias_value":"FAT2EM4H","created_at":"2026-05-18T12:29:19.899920+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/FAT2EM4HVPZFBI5COKU4T4RFOW","json":"https://pith.science/pith/FAT2EM4HVPZFBI5COKU4T4RFOW.json","graph_json":"https://pith.science/api/pith-number/FAT2EM4HVPZFBI5COKU4T4RFOW/graph.json","events_json":"https://pith.science/api/pith-number/FAT2EM4HVPZFBI5COKU4T4RFOW/events.json","paper":"https://pith.science/paper/FAT2EM4H"},"agent_actions":{"view_html":"https://pith.science/pith/FAT2EM4HVPZFBI5COKU4T4RFOW","download_json":"https://pith.science/pith/FAT2EM4HVPZFBI5COKU4T4RFOW.json","view_paper":"https://pith.science/paper/FAT2EM4H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.01874&json=true","fetch_graph":"https://pith.science/api/pith-number/FAT2EM4HVPZFBI5COKU4T4RFOW/graph.json","fetch_events":"https://pith.science/api/pith-number/FAT2EM4HVPZFBI5COKU4T4RFOW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FAT2EM4HVPZFBI5COKU4T4RFOW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FAT2EM4HVPZFBI5COKU4T4RFOW/action/storage_attestation","attest_author":"https://pith.science/pith/FAT2EM4HVPZFBI5COKU4T4RFOW/action/author_attestation","sign_citation":"https://pith.science/pith/FAT2EM4HVPZFBI5COKU4T4RFOW/action/citation_signature","submit_replication":"https://pith.science/pith/FAT2EM4HVPZFBI5COKU4T4RFOW/action/replication_record"}},"created_at":"2026-05-18T01:27:21.888521+00:00","updated_at":"2026-05-18T01:27:21.888521+00:00"}