Problem Detection in Real-Time Systems by Trace Analysis
Bibliographic record
Abstract
This paper focuses on the analysis of execution traces for real-time systems. Kernel tracing can provide useful information, without having to instrument the applications studied. However, the generated traces are often very large. The challenge is to retrieve only relevant data in order to find quickly complex or erratic real-time problems. We propose a new approach to help finding those problems. First, we provide a way to define the execution model of real-time tasks with the optional suggestions of a pattern discovery algorithm. Then, we show the resulting real-time jobs in a Comparison View, to highlight those that are problematic. Once some jobs that present irregularities are selected, different analyses are executed on the corresponding trace segments instead of the whole trace. This allows saving huge amount of time and execute more complex analyses. Our main contribution is to combine the critical path analysis with the scheduling information to detect scheduling problems. The efficiency of the proposed method is demonstrated with two test cases, where problems that were difficult to identify were found in a few minutes.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".