LogAssist: Assisting Log Analysis Through Log Summarization
Bibliographic record
Abstract
Logs contain valuable information about the runtime behaviors of software systems. Thus, practitioners rely on logs for various tasks such as debugging, system comprehension, and anomaly detection. However, logs are difficult to analyze due to their unstructured nature and large size. In this paper, we propose a novel approach calledLogAssistthat assists practitioners with log analysis.LogAssistprovides an organized and concise view of logs by first grouping logs into event sequences (i.e., workflows), which better illustrate the system runtime execution paths. Then,LogAssistcompresses the log events in workflows by hiding consecutive events and applying n-gram modeling to identify common event sequences. We evaluatedLogAssiston logs generated by one enterprise and two open source systems. We find thatLogAssistcan reduce the number of log events that practitioners need to investigate by up to 99 percent. Through a user study with 19 participants, we find thatLogAssistcan assist practitioners by reducing the time required for log analysis tasks by an average of 40 percent. The participants also ratedLogAssistan average of 4.53 out of 5 for improving their experiences of performing log analysis. Finally, we document our experiences and lessons learned from developing and adoptingLogAssistin practice. We believe thatLogAssistand our reported experiences may lay the basis for future analysis and interactive exploration on logs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".