Abstracting Execution Logs to Execution Events for Enterprise Applications (Short Paper)
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
Monitoring the execution of large enterprise systems is needed to ensure that such complex systems are performing as expected. However, common techniques for monitoring, such as code instrumentation and profiling have significant performance overhead, and require access to the source code and to system experts. In this paper, we propose using execution logs to monitor the execution of applications. Unfortunately, execution logs are not designed for monitoring purposes. Each occurrence of an execution event results in a different log line, since a log line contains dynamic information which varies for each occurrence of the event. We propose an approach which abstracts log lines to a set of execution events. Our approach can handle log lines without having strict requirements on the format of a log line. Through a case study on a large enterprise application, we demonstrate that our approach performs well when abstracting execution logs for large enterprise applications. We compare our approach against the SLCT tool which is commonly used to find line patterns in logs.
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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 | 0.000 | 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