{"ID":2838054,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18528","arxiv_id":"2511.18528","title":"End-to-End Automated Logging via Multi-Agent Framework","abstract":"Software logging is critical for system observability, yet developers face a dual crisis of costly overlogging and risky underlogging. Existing automated logging tools often overlook the fundamental whether-to-log decision and struggle with the composite nature of logging. In this paper, we propose Autologger, a novel hybrid framework that addresses the complete the end-to-end logging pipeline. Autologger first employs a fine-tuned classifier, the Judger, to accurately determine if a method requires new logging statements. If logging is needed, a multi-agent system is activated. The system includes specialized agents: a Locator dedicated to determining where to log, and a Generator focused on what to log. These agents work together, utilizing our designed program analysis and retrieval tools. We evaluate Autologger on a large corpus from three mature open-source projects against state-of-the-art baselines. Our results show that Autologger achieves 96.63\\% F1-score on the crucial whether-to-log decision. In an end-to-end setting, Autologger improves the overall quality of generated logging statements by 16.13\\% over the strongest baseline, as measured by an LLM-as-a-judge score. We also demonstrate that our framework is generalizable, consistently boosting the performance of various backbone LLMs.","short_abstract":"Software logging is critical for system observability, yet developers face a dual crisis of costly overlogging and risky underlogging. Existing automated logging tools often overlook the fundamental whether-to-log decision and struggle with the composite nature of logging. In this paper, we propose Autologger, a novel...","url_abs":"https://arxiv.org/abs/2511.18528","url_pdf":"https://arxiv.org/pdf/2511.18528v1","authors":"[\"Renyi Zhong\",\"Yintong Huo\",\"Wenwei Gu\",\"Yichen Li\",\"Michael R. Lyu\"]","published":"2025-11-23T16:45:30Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\"]","has_code":false}