{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LIRUSWJRPPJ57PH46F3T2LW6IA","short_pith_number":"pith:LIRUSWJR","schema_version":"1.0","canonical_sha256":"5a234959317bd3dfbcfcf1773d2ede400786596dc6cc1bceeafc8e5eb8bee135","source":{"kind":"arxiv","id":"2504.18955","version":2},"attestation_state":"computed","paper":{"title":"A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"quant-ph","authors_text":"Antonio Trovato, Dario Di Nucci, Martin Beseda","submitted_at":"2025-04-26T15:38:01Z","abstract_excerpt":"Regression testing is key in verifying that software works correctly after changes. However, running the entire regression test suite can be impractical and expensive, especially for large-scale systems. Test suite optimization methods are highly effective but often become infeasible due to their high computational demands. In previous work, Trovato et al. proposed SelectQA, an approach based on quantum annealing that outperforms the traditional state-of-the-art methods, i.e., Additional Greedy and DIV-GA, in efficiency. This work envisions the usage of Quantum Approximate Optimization Algorit"},"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":"2504.18955","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-04-26T15:38:01Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"1691c6eb389d737f1cfe07e03fdbaa7c68454aa9c5b693af8e2cf6ae72ed7b2d","abstract_canon_sha256":"55e05393dd46f780049ffed52835edfeb3d55e8f2d13011d46b97056a700243a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:56:18.409932Z","signature_b64":"n9Et6TVz2o/iy/A0bj3+HvOjST6Z1m4B5s+VQrj8Q50ZYbPWVq6zogzvTyBEvL9soNfHFUeAbQBdMLZQ2XBkDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a234959317bd3dfbcfcf1773d2ede400786596dc6cc1bceeafc8e5eb8bee135","last_reissued_at":"2026-07-05T10:56:18.409188Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:56:18.409188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"quant-ph","authors_text":"Antonio Trovato, Dario Di Nucci, Martin Beseda","submitted_at":"2025-04-26T15:38:01Z","abstract_excerpt":"Regression testing is key in verifying that software works correctly after changes. However, running the entire regression test suite can be impractical and expensive, especially for large-scale systems. Test suite optimization methods are highly effective but often become infeasible due to their high computational demands. In previous work, Trovato et al. proposed SelectQA, an approach based on quantum annealing that outperforms the traditional state-of-the-art methods, i.e., Additional Greedy and DIV-GA, in efficiency. This work envisions the usage of Quantum Approximate Optimization Algorit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.18955","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/2504.18955/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":"2504.18955","created_at":"2026-07-05T10:56:18.409247+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.18955v2","created_at":"2026-07-05T10:56:18.409247+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.18955","created_at":"2026-07-05T10:56:18.409247+00:00"},{"alias_kind":"pith_short_12","alias_value":"LIRUSWJRPPJ5","created_at":"2026-07-05T10:56:18.409247+00:00"},{"alias_kind":"pith_short_16","alias_value":"LIRUSWJRPPJ57PH4","created_at":"2026-07-05T10:56:18.409247+00:00"},{"alias_kind":"pith_short_8","alias_value":"LIRUSWJR","created_at":"2026-07-05T10:56:18.409247+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.23674","citing_title":"Quantum-Based Software Engineering","ref_index":48,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LIRUSWJRPPJ57PH46F3T2LW6IA","json":"https://pith.science/pith/LIRUSWJRPPJ57PH46F3T2LW6IA.json","graph_json":"https://pith.science/api/pith-number/LIRUSWJRPPJ57PH46F3T2LW6IA/graph.json","events_json":"https://pith.science/api/pith-number/LIRUSWJRPPJ57PH46F3T2LW6IA/events.json","paper":"https://pith.science/paper/LIRUSWJR"},"agent_actions":{"view_html":"https://pith.science/pith/LIRUSWJRPPJ57PH46F3T2LW6IA","download_json":"https://pith.science/pith/LIRUSWJRPPJ57PH46F3T2LW6IA.json","view_paper":"https://pith.science/paper/LIRUSWJR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.18955&json=true","fetch_graph":"https://pith.science/api/pith-number/LIRUSWJRPPJ57PH46F3T2LW6IA/graph.json","fetch_events":"https://pith.science/api/pith-number/LIRUSWJRPPJ57PH46F3T2LW6IA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LIRUSWJRPPJ57PH46F3T2LW6IA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LIRUSWJRPPJ57PH46F3T2LW6IA/action/storage_attestation","attest_author":"https://pith.science/pith/LIRUSWJRPPJ57PH46F3T2LW6IA/action/author_attestation","sign_citation":"https://pith.science/pith/LIRUSWJRPPJ57PH46F3T2LW6IA/action/citation_signature","submit_replication":"https://pith.science/pith/LIRUSWJRPPJ57PH46F3T2LW6IA/action/replication_record"}},"created_at":"2026-07-05T10:56:18.409247+00:00","updated_at":"2026-07-05T10:56:18.409247+00:00"}