{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JMSQMWEPLERIKOKAJLN5ATOEME","short_pith_number":"pith:JMSQMWEP","schema_version":"1.0","canonical_sha256":"4b2506588f59228539404adbd04dc46123da10fa3da507b2a2537f53819beecc","source":{"kind":"arxiv","id":"2403.16218","version":4},"attestation_state":"computed","paper":{"title":"CoverUp: Effective High Coverage Test Generation for Python","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.PL"],"primary_cat":"cs.SE","authors_text":"Emery D. Berger, Juan Altmayer Pizzorno","submitted_at":"2024-03-24T16:18:27Z","abstract_excerpt":"Testing is an essential part of software development. Test generation tools attempt to automate the otherwise labor-intensive task of test creation, but generating high-coverage tests remains challenging. This paper proposes CoverUp, a novel approach to driving the generation of high-coverage Python regression tests. CoverUp combines coverage analysis, code context, and feedback in prompts that iteratively guide the LLM to generate tests that improve line and branch coverage. We evaluate our prototype CoverUp implementation across a benchmark of challenging code derived from open-source Python"},"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":"2403.16218","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-03-24T16:18:27Z","cross_cats_sorted":["cs.AI","cs.LG","cs.PL"],"title_canon_sha256":"5c79fbe4210ab30b9a74729e1d37b2c3c7ba648b4f5691fb3d8b733380f93e15","abstract_canon_sha256":"a80aeeabf83aa818206e557908541d721f085ec96a607c2d7dbabef9c92db0da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:00:36.674514Z","signature_b64":"BXU6aR/oy2B1gkYpRsYlO2e19uV/QZGB/AlT7AdLsC6W+GHuHhFdvTBYjJxp2ZyUnXmuPEAUdG+D6l38NSc1AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b2506588f59228539404adbd04dc46123da10fa3da507b2a2537f53819beecc","last_reissued_at":"2026-07-05T11:00:36.673952Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:00:36.673952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CoverUp: Effective High Coverage Test Generation for Python","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.PL"],"primary_cat":"cs.SE","authors_text":"Emery D. Berger, Juan Altmayer Pizzorno","submitted_at":"2024-03-24T16:18:27Z","abstract_excerpt":"Testing is an essential part of software development. Test generation tools attempt to automate the otherwise labor-intensive task of test creation, but generating high-coverage tests remains challenging. This paper proposes CoverUp, a novel approach to driving the generation of high-coverage Python regression tests. CoverUp combines coverage analysis, code context, and feedback in prompts that iteratively guide the LLM to generate tests that improve line and branch coverage. We evaluate our prototype CoverUp implementation across a benchmark of challenging code derived from open-source Python"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.16218","kind":"arxiv","version":4},"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/2403.16218/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":"2403.16218","created_at":"2026-07-05T11:00:36.674007+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.16218v4","created_at":"2026-07-05T11:00:36.674007+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.16218","created_at":"2026-07-05T11:00:36.674007+00:00"},{"alias_kind":"pith_short_12","alias_value":"JMSQMWEPLERI","created_at":"2026-07-05T11:00:36.674007+00:00"},{"alias_kind":"pith_short_16","alias_value":"JMSQMWEPLERIKOKA","created_at":"2026-07-05T11:00:36.674007+00:00"},{"alias_kind":"pith_short_8","alias_value":"JMSQMWEP","created_at":"2026-07-05T11:00:36.674007+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.15229","citing_title":"PBT-Bench: Benchmarking AI Agents on Property-Based Testing","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00530","citing_title":"Sakura: An Approach for Generating Complex Tests from Natural Language Test Descriptions","ref_index":64,"is_internal_anchor":false},{"citing_arxiv_id":"2412.15931","citing_title":"Large Language Model assisted Hybrid Fuzzing","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15229","citing_title":"PBT-Bench: Benchmarking AI Agents on Property-Based Testing","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15229","citing_title":"PBT-Bench: Benchmarking AI Agents on Property-Based Testing","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2506.02954","citing_title":"Mutation-Guided Unit Test Generation with a Large Language Model","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2407.01489","citing_title":"Agentless: Demystifying LLM-based Software Engineering Agents","ref_index":82,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14590","citing_title":"AgileLog: A Forkable Shared Log for Agents on Data Streams","ref_index":97,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JMSQMWEPLERIKOKAJLN5ATOEME","json":"https://pith.science/pith/JMSQMWEPLERIKOKAJLN5ATOEME.json","graph_json":"https://pith.science/api/pith-number/JMSQMWEPLERIKOKAJLN5ATOEME/graph.json","events_json":"https://pith.science/api/pith-number/JMSQMWEPLERIKOKAJLN5ATOEME/events.json","paper":"https://pith.science/paper/JMSQMWEP"},"agent_actions":{"view_html":"https://pith.science/pith/JMSQMWEPLERIKOKAJLN5ATOEME","download_json":"https://pith.science/pith/JMSQMWEPLERIKOKAJLN5ATOEME.json","view_paper":"https://pith.science/paper/JMSQMWEP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.16218&json=true","fetch_graph":"https://pith.science/api/pith-number/JMSQMWEPLERIKOKAJLN5ATOEME/graph.json","fetch_events":"https://pith.science/api/pith-number/JMSQMWEPLERIKOKAJLN5ATOEME/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JMSQMWEPLERIKOKAJLN5ATOEME/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JMSQMWEPLERIKOKAJLN5ATOEME/action/storage_attestation","attest_author":"https://pith.science/pith/JMSQMWEPLERIKOKAJLN5ATOEME/action/author_attestation","sign_citation":"https://pith.science/pith/JMSQMWEPLERIKOKAJLN5ATOEME/action/citation_signature","submit_replication":"https://pith.science/pith/JMSQMWEPLERIKOKAJLN5ATOEME/action/replication_record"}},"created_at":"2026-07-05T11:00:36.674007+00:00","updated_at":"2026-07-05T11:00:36.674007+00:00"}