{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LMY4UGXTJDQVDOCE7SIJEYTTP6","short_pith_number":"pith:LMY4UGXT","schema_version":"1.0","canonical_sha256":"5b31ca1af348e151b844fc909262737fb35984265d94c1ed7f7418930afb49da","source":{"kind":"arxiv","id":"2504.18804","version":1},"attestation_state":"computed","paper":{"title":"Can We Enhance Bug Report Quality Using LLMs?: An Empirical Study of LLM-Based Bug Report Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Gouri Ginde, Jagrit Acharya","submitted_at":"2025-04-26T05:15:53Z","abstract_excerpt":"Bug reports contain the information developers need to triage and fix software bugs. However, unclear, incomplete, or ambiguous information may lead to delays and excessive manual effort spent on bug triage and resolution. In this paper, we explore whether Instruction fine-tuned Large Language Models (LLMs) can automatically transform casual, unstructured bug reports into high-quality, structured bug reports adhering to a standard template. We evaluate three open-source instruction-tuned LLMs (\\emph{Qwen 2.5, Mistral, and Llama 3.2}) against ChatGPT-4o, measuring performance on established met"},"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":"2504.18804","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2025-04-26T05:15:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ab6d5c91b76a5d63a7fd0b2a6f13e231cede375acd98e295aaa29db6ddee0a38","abstract_canon_sha256":"32d63c4472114dbed8de91fa98b02ba72992157882cea2d0f5fdfbb3de16e6dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:21.642948Z","signature_b64":"SUUsMJuRaQncomH+z16kjIW5h2WYCWILvn3vbxViyZlb4XYYqhbrlXowtfLXTIGvlH1Bj/1FmK3CUrZEzZmeDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b31ca1af348e151b844fc909262737fb35984265d94c1ed7f7418930afb49da","last_reissued_at":"2026-07-05T10:54:21.642474Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:21.642474Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can We Enhance Bug Report Quality Using LLMs?: An Empirical Study of LLM-Based Bug Report Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Gouri Ginde, Jagrit Acharya","submitted_at":"2025-04-26T05:15:53Z","abstract_excerpt":"Bug reports contain the information developers need to triage and fix software bugs. However, unclear, incomplete, or ambiguous information may lead to delays and excessive manual effort spent on bug triage and resolution. In this paper, we explore whether Instruction fine-tuned Large Language Models (LLMs) can automatically transform casual, unstructured bug reports into high-quality, structured bug reports adhering to a standard template. We evaluate three open-source instruction-tuned LLMs (\\emph{Qwen 2.5, Mistral, and Llama 3.2}) against ChatGPT-4o, measuring performance on established met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.18804","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/2504.18804/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":"2504.18804","created_at":"2026-07-05T10:54:21.642535+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.18804v1","created_at":"2026-07-05T10:54:21.642535+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.18804","created_at":"2026-07-05T10:54:21.642535+00:00"},{"alias_kind":"pith_short_12","alias_value":"LMY4UGXTJDQV","created_at":"2026-07-05T10:54:21.642535+00:00"},{"alias_kind":"pith_short_16","alias_value":"LMY4UGXTJDQVDOCE","created_at":"2026-07-05T10:54:21.642535+00:00"},{"alias_kind":"pith_short_8","alias_value":"LMY4UGXT","created_at":"2026-07-05T10:54:21.642535+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07882","citing_title":"Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2604.26142","citing_title":"ImproBR: Bug Report Improver Using LLMs","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23361","citing_title":"An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LMY4UGXTJDQVDOCE7SIJEYTTP6","json":"https://pith.science/pith/LMY4UGXTJDQVDOCE7SIJEYTTP6.json","graph_json":"https://pith.science/api/pith-number/LMY4UGXTJDQVDOCE7SIJEYTTP6/graph.json","events_json":"https://pith.science/api/pith-number/LMY4UGXTJDQVDOCE7SIJEYTTP6/events.json","paper":"https://pith.science/paper/LMY4UGXT"},"agent_actions":{"view_html":"https://pith.science/pith/LMY4UGXTJDQVDOCE7SIJEYTTP6","download_json":"https://pith.science/pith/LMY4UGXTJDQVDOCE7SIJEYTTP6.json","view_paper":"https://pith.science/paper/LMY4UGXT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.18804&json=true","fetch_graph":"https://pith.science/api/pith-number/LMY4UGXTJDQVDOCE7SIJEYTTP6/graph.json","fetch_events":"https://pith.science/api/pith-number/LMY4UGXTJDQVDOCE7SIJEYTTP6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LMY4UGXTJDQVDOCE7SIJEYTTP6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LMY4UGXTJDQVDOCE7SIJEYTTP6/action/storage_attestation","attest_author":"https://pith.science/pith/LMY4UGXTJDQVDOCE7SIJEYTTP6/action/author_attestation","sign_citation":"https://pith.science/pith/LMY4UGXTJDQVDOCE7SIJEYTTP6/action/citation_signature","submit_replication":"https://pith.science/pith/LMY4UGXTJDQVDOCE7SIJEYTTP6/action/replication_record"}},"created_at":"2026-07-05T10:54:21.642535+00:00","updated_at":"2026-07-05T10:54:21.642535+00:00"}