An automated approach for abstracting execution logs to execution events
Bibliographic record
Abstract
Abstract Execution logs are generated by output statements that developers insert into the source code. Execution logs are widely available and are helpful in monitoring, remote issue resolution, and system understanding of complex enterprise applications. There are many proposals for standardized log formats such as the W3C and SNMP formats. However, most applications usead hocnon‐standardized logging formats. Automated analysis of such logs is complex due to the loosely defined structure and a large non‐fixed vocabulary of words. The large volume of logs, produced by enterprise applications, limits the usefulness of manual analysis techniques. Automated techniques are needed to uncover the structure of execution logs. Using the uncovered structure, sophisticated analysis of logs can be performed. In this paper, we propose a log abstraction technique that recognizes the internal structure of each log line. Using the recovered structure, log lines can be easily summarized and categorized to help comprehend and investigate the complex behavior of large software applications. Our proposed approach handles free‐form log lines with minimal requirements on the format of a log line. Through a case study using log files from four enterprise applications, we demonstrate that our approach abstracts log files of different complexities with high precision and recall. Copyright © 2008 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".