An Adaptive Logging System (ALS): Enhancing Software Logging with Reinforcement Learning Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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 log-placement 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 resource-limited 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it