An Empirical Study on Leveraging Logs for Debugging Production Failures
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
In modern software development, maintenance is one of the most expensive processes. When end-users encounter software defects, they report the bug to developers by specifying the expected behavior and error messages (e.g., log message). Then, they wait for a bug fix from the developers. However, on the developers' side, it can be very challenging and expensive to debug the problem. To fix the bugs, developers often have to play the role of detectives: seeking clues in the user-reported logs files or stack trace in a snapshot of specific system execution. This debugging process may take several hours or even days. In this paper, we first look at the usefulness of the user-reported logs. Then, we propose an automated approach to assist the debugging process by reconstructing the execution path. Through the analysis, our investigation shows that 31% of the time, developer further requests logs from the reporter. Moreover, our preliminary results show that the reconducted path illustrates the user's execution. We believe that our approach proposes a novel solution in debugging production failures.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 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