An Effective Approach for Parsing Large Log Files
Bibliographic record
Abstract
Because of their contribution to the overall reliability assurance process, software logs have become important data assets for the analysis of software systems. Logs are often the only data points that can shed light on how a software system behaves once deployed. Unfortunately, logs are often unstructured data items, hindering viable analysis of their content. There are studies that aim to automatically parse large log files. The primary goal is to create templates from raw log data samples that can later be used to recognize future logs. In this paper, we propose ULP, a Unified Log Parsing tool, which is highly accurate and efficient. ULP combines string matching and local frequency analysis to parse large log files in an efficient manner. First, log events are organized into groups using a text processing method. Frequency analysis is then applied locally to instances of the same group to identify static and dynamic content of log events. When applied to 10 log datasets of the LogPai benchmark, ULP achieves an average accuracy of 89.2%, which outperforms the accuracy of four leading log parsing tools, namely Drain, Logram, SPELL and AEL. Additionally, ULP can parse up to four million log events in less than 3 minutes. ULP is available online as an open source and can be readily used by practitioners and researchers to parse effectively and efficiently large log files so as to support log analysis tasks.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".