An Adaptive Logging System (ALS): Enhancing Software Logging with Reinforcement Learning Techniques

Abstract

The efficient management of software logs is crucial in software performance evaluation, enabling detailed examination of runtime information for postmortem analysis. Recognizing the importance of logs and the challenges developers face in making informed logplacement decisions, there is a clear need for a robust log-placement framework that supports developers. Existing frameworks, however, are limited by their inability to adapt to customized logging objectives, a concern highlighted by our industrial partner, Ciena, who required a system for their specific logging goals in resourcelimited environments like routers. Moreover, these frameworks often show poor cross-project consistency. This study introduces a novel performance logging objective designed to uncover potential performance-bugs, categorized into three classes - Loops, Synchronization, and API Misuses - and defines 12 source code features for their detection. We present an Adaptive Logging System (ALS), based on reinforcement learning, which adjusts to specified logging objectives, particularly for identifying performance-bugs. This framework, not restricted to specific projects, demonstrates stable cross-project performance.We trained and evaluated ALS on Python source code from 17 diverse open-source projects within the Apache and Django ecosystems. Our findings suggest that ALS has the potential to significantly enhance current logging practices by providing a more targeted, efficient, and context-aware logging approach, particularly beneficial for our industry partner who requires a flexible system that adapts to varied performance objectives and logging needs in their unique operational environments.