Log Sculptor: Making Logs Great Again

Aditya Kulkarni, Prathit Mehta, Ravi Kumar Singh, Mayank Mishra, Shruti Kunde, Rekha Singhal, Manoj Nambiar

Abstract: In production environments where applications generate vast data, minimizing downtime is critical. However, large-scale log data can overwhelm storage and computational resources, making system stability challenging to maintain. Poor logging practices worsen this by creating excessive, irrelevant, or unstructured logs that hinder efficient application-crash detection and resolution. This is an enterprise-wide issue often leading to resource exhaustion and prolonged downtime.To address this, we propose Log Sculptor, a tool that leverages GenAI (Large Language Models) to proactively analyze, identify, and improve logging practices in code. It provides recommendations for log statement adjustments (rectifications, additions, removals) and can apply these to produce code with optimized logs. Log Sculptor operates on a prompt-based approach.We benchmark Log Sculptor on nine open-source codebases annotated by industry practitioners for logging practices. It achieves results comparable to human experts and suggests further refinements to enhance logging. We analyze the cost-benefit trade-offs, demonstrating the potential of GenAI in transforming logging practices, improving debugging efficiency, reducing downtime, and increasing reliability in production environments.