{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XE43IU6ZOS7STVLELYELTS3LBW","short_pith_number":"pith:XE43IU6Z","schema_version":"1.0","canonical_sha256":"b939b453d974bf29d5645e08b9cb6b0dbdfd99efc92680efe3762656c5c36e44","source":{"kind":"arxiv","id":"2503.04301","version":1},"attestation_state":"computed","paper":{"title":"Simple Fault Localization using Execution Traces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Julian Aron Prenner, Romain Robbes","submitted_at":"2025-03-06T10:41:12Z","abstract_excerpt":"Traditional spectrum-based fault localization (SBFL) exploits differences in a program's coverage spectrum when run on passing and failing test cases. However, such runs can provide a wealth of additional information beyond mere coverage. Working with thousands of execution traces of short programs submitted to competitive programming contests and leveraging machine learning and additional runtime, control-flow and lexical features, we present simple ways to improve SBFL. We also propose a simple trick to integrate context information. Our approach outperforms SBFL formulae such as Ochiai on o"},"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":"2503.04301","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-03-06T10:41:12Z","cross_cats_sorted":[],"title_canon_sha256":"e1593f485d559111b21e0047c32fc38eea29a47b314799353218d7daba21b810","abstract_canon_sha256":"0ef50f22e542787930f49ea0785c12f5da17410aaa6edf92b788e40c90ffee4f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:25:34.838067Z","signature_b64":"PPYVyspb83+Q9tboqjACUEW7/snaWesu2i/IzLDs8o1bgzGq98EtmNqjFjMzwKA8a92dx6aNqI8PIoQarB3PDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b939b453d974bf29d5645e08b9cb6b0dbdfd99efc92680efe3762656c5c36e44","last_reissued_at":"2026-07-05T10:25:34.837369Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:25:34.837369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simple Fault Localization using Execution Traces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Julian Aron Prenner, Romain Robbes","submitted_at":"2025-03-06T10:41:12Z","abstract_excerpt":"Traditional spectrum-based fault localization (SBFL) exploits differences in a program's coverage spectrum when run on passing and failing test cases. However, such runs can provide a wealth of additional information beyond mere coverage. Working with thousands of execution traces of short programs submitted to competitive programming contests and leveraging machine learning and additional runtime, control-flow and lexical features, we present simple ways to improve SBFL. We also propose a simple trick to integrate context information. Our approach outperforms SBFL formulae such as Ochiai on o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04301","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/2503.04301/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":"2503.04301","created_at":"2026-07-05T10:25:34.837470+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.04301v1","created_at":"2026-07-05T10:25:34.837470+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04301","created_at":"2026-07-05T10:25:34.837470+00:00"},{"alias_kind":"pith_short_12","alias_value":"XE43IU6ZOS7S","created_at":"2026-07-05T10:25:34.837470+00:00"},{"alias_kind":"pith_short_16","alias_value":"XE43IU6ZOS7STVLE","created_at":"2026-07-05T10:25:34.837470+00:00"},{"alias_kind":"pith_short_8","alias_value":"XE43IU6Z","created_at":"2026-07-05T10:25:34.837470+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/XE43IU6ZOS7STVLELYELTS3LBW","json":"https://pith.science/pith/XE43IU6ZOS7STVLELYELTS3LBW.json","graph_json":"https://pith.science/api/pith-number/XE43IU6ZOS7STVLELYELTS3LBW/graph.json","events_json":"https://pith.science/api/pith-number/XE43IU6ZOS7STVLELYELTS3LBW/events.json","paper":"https://pith.science/paper/XE43IU6Z"},"agent_actions":{"view_html":"https://pith.science/pith/XE43IU6ZOS7STVLELYELTS3LBW","download_json":"https://pith.science/pith/XE43IU6ZOS7STVLELYELTS3LBW.json","view_paper":"https://pith.science/paper/XE43IU6Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.04301&json=true","fetch_graph":"https://pith.science/api/pith-number/XE43IU6ZOS7STVLELYELTS3LBW/graph.json","fetch_events":"https://pith.science/api/pith-number/XE43IU6ZOS7STVLELYELTS3LBW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XE43IU6ZOS7STVLELYELTS3LBW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XE43IU6ZOS7STVLELYELTS3LBW/action/storage_attestation","attest_author":"https://pith.science/pith/XE43IU6ZOS7STVLELYELTS3LBW/action/author_attestation","sign_citation":"https://pith.science/pith/XE43IU6ZOS7STVLELYELTS3LBW/action/citation_signature","submit_replication":"https://pith.science/pith/XE43IU6ZOS7STVLELYELTS3LBW/action/replication_record"}},"created_at":"2026-07-05T10:25:34.837470+00:00","updated_at":"2026-07-05T10:25:34.837470+00:00"}