High-quality logging is critical for the reliability of cloud services, yet the industrial process for improving it is typically manual, reactive, and unscalable. Existing automated tools inherit this reactive nature, failing to answer the crucial whether-to-log question and are constrained to simple logging statement insertion, thus addressing only a fraction of the real-world logging improvement.

To address these gaps and cope with logging debt in large-scale codebases, we propose LogImprover, a framework powered by Large Language Models (LLMs) that automates proactive logging quality improvement. LogImprover introduces two paradigm shifts: from reactive generation to proactive discovery, and from simple insertion to holistic logging patch generation. First, it identifies potential logging gaps based on principles distilled from industrial best practices. Then, it grounds each candidate through a cascading, structure-aware RAG module. Next, it prunes false positives by analyzing call-stack logging responsibilities and implicit logger inheritance. Finally, it generates holistic and explainable logging patches that reflect real-world development practices.

Our evaluation provides dual confirmation of its effectiveness: LogImprover significantly outperforms state-of-the-art baselines in closed-world experiments and achieves 68.12% developer acceptance rate in its real-world deployment. This success demonstrates the practical value of automating the entire logging quality improvement lifecycle, from discovery to recommendation.