AML: An accuracy metric model for effective evaluation of log parsing techniques
Bibliographic record
Abstract
Logs are essential for the maintenance of large software systems. Software engineers often analyze logs for debugging, root cause analysis , and anomaly detection tasks. Logs, however, are partly structured, making the extraction of useful information from massive log files a challenging task. Recently, many log parsing techniques have been proposed to automatically extract log templates from unstructured log files. These parsers, however, are evaluated using different accuracy metrics. In this paper, we show that these metrics have several drawbacks, making it challenging to understand the strengths and limitations of existing parsers. To address this, we propose a novel accuracy metric, called AML (Accuracy Metric for Log Parsing). AML is a robust accuracy metric that is inspired by research in the field of remote sensing . It is based on measuring omission and commission errors. We use AML to assess the accuracy of 14 log parsing tools applied to the parsing of 16 log datasets. We also show how AML compares to existing accuracy metrics. Our findings demonstrate that AML is a promising accuracy metric for log parsing compared to alternative solutions, which enables a comprehensive evaluation of log parsing tools to help better decision-making in selecting and improving log parsing techniques.
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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 | 0.019 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".