{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EMDHQUA2M3JI5VZ4GOUC7EOH3C","short_pith_number":"pith:EMDHQUA2","schema_version":"1.0","canonical_sha256":"230678501a66d28ed73c33a82f91c7d880a5a3b76df1c92761cc44c56a4045f9","source":{"kind":"arxiv","id":"2505.16590","version":3},"attestation_state":"computed","paper":{"title":"Larger Is Not Always Better: Exploring Small Open-source Language Models in Logging Statement Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Guangba Yu, Jinxi Kuang, Michael R. Lyu, Renyi Zhong, Wenwei Gu, Yichen Li, Yintong Huo","submitted_at":"2025-05-22T12:26:53Z","abstract_excerpt":"Developers use logging statements to create logs that document system behavior and aid in software maintenance. As such, high-quality logging is essential for effective maintenance; however, manual logging often leads to errors and inconsistency. Recent methods emphasize using large language models (LLMs) for automated logging statement generation, but these present privacy and resource issues, hindering their suitability for enterprise use. This paper presents the first large-scale empirical study evaluating small open-source language models (SOLMs) for automated logging statement generation."},"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":"2505.16590","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2025-05-22T12:26:53Z","cross_cats_sorted":[],"title_canon_sha256":"06899e772a31ac2cc333f5c898d04e3f50771cfa65506a0a3da139bb95891b5a","abstract_canon_sha256":"8a6c835b4ffc7b020c03f1d0e925ce2ec1798ba1bce28234944eee2641cec323"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:04:35.058284Z","signature_b64":"7GSorRxW5AW6i9fA2oEZTuP7448FjOuIuAZtUDYMK2EvbzUYkJfrnjyZ4CgYlq4uB0plVT+OpGEfpCylhZFYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"230678501a66d28ed73c33a82f91c7d880a5a3b76df1c92761cc44c56a4045f9","last_reissued_at":"2026-07-05T12:04:35.057785Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:04:35.057785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Larger Is Not Always Better: Exploring Small Open-source Language Models in Logging Statement Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Guangba Yu, Jinxi Kuang, Michael R. Lyu, Renyi Zhong, Wenwei Gu, Yichen Li, Yintong Huo","submitted_at":"2025-05-22T12:26:53Z","abstract_excerpt":"Developers use logging statements to create logs that document system behavior and aid in software maintenance. As such, high-quality logging is essential for effective maintenance; however, manual logging often leads to errors and inconsistency. Recent methods emphasize using large language models (LLMs) for automated logging statement generation, but these present privacy and resource issues, hindering their suitability for enterprise use. This paper presents the first large-scale empirical study evaluating small open-source language models (SOLMs) for automated logging statement generation."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16590","kind":"arxiv","version":3},"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/2505.16590/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":"2505.16590","created_at":"2026-07-05T12:04:35.057845+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.16590v3","created_at":"2026-07-05T12:04:35.057845+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16590","created_at":"2026-07-05T12:04:35.057845+00:00"},{"alias_kind":"pith_short_12","alias_value":"EMDHQUA2M3JI","created_at":"2026-07-05T12:04:35.057845+00:00"},{"alias_kind":"pith_short_16","alias_value":"EMDHQUA2M3JI5VZ4","created_at":"2026-07-05T12:04:35.057845+00:00"},{"alias_kind":"pith_short_8","alias_value":"EMDHQUA2","created_at":"2026-07-05T12:04:35.057845+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.20211","citing_title":"Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs","ref_index":66,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EMDHQUA2M3JI5VZ4GOUC7EOH3C","json":"https://pith.science/pith/EMDHQUA2M3JI5VZ4GOUC7EOH3C.json","graph_json":"https://pith.science/api/pith-number/EMDHQUA2M3JI5VZ4GOUC7EOH3C/graph.json","events_json":"https://pith.science/api/pith-number/EMDHQUA2M3JI5VZ4GOUC7EOH3C/events.json","paper":"https://pith.science/paper/EMDHQUA2"},"agent_actions":{"view_html":"https://pith.science/pith/EMDHQUA2M3JI5VZ4GOUC7EOH3C","download_json":"https://pith.science/pith/EMDHQUA2M3JI5VZ4GOUC7EOH3C.json","view_paper":"https://pith.science/paper/EMDHQUA2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.16590&json=true","fetch_graph":"https://pith.science/api/pith-number/EMDHQUA2M3JI5VZ4GOUC7EOH3C/graph.json","fetch_events":"https://pith.science/api/pith-number/EMDHQUA2M3JI5VZ4GOUC7EOH3C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EMDHQUA2M3JI5VZ4GOUC7EOH3C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EMDHQUA2M3JI5VZ4GOUC7EOH3C/action/storage_attestation","attest_author":"https://pith.science/pith/EMDHQUA2M3JI5VZ4GOUC7EOH3C/action/author_attestation","sign_citation":"https://pith.science/pith/EMDHQUA2M3JI5VZ4GOUC7EOH3C/action/citation_signature","submit_replication":"https://pith.science/pith/EMDHQUA2M3JI5VZ4GOUC7EOH3C/action/replication_record"}},"created_at":"2026-07-05T12:04:35.057845+00:00","updated_at":"2026-07-05T12:04:35.057845+00:00"}