Execution trace‐based model verification to analyze multicore and real‐time systems
Bibliographic record
Abstract
Abstract As a key part of model‐driven development, modeling allows users to represent the application workflow or to automatically generate source code. This is convenient for developers, particularly to create or improve real‐time applications embedded in complex systems. Multicore systems are difficult to debug because the concurrently running processes can interfere with each other. In real‐time systems, timing constraints add to the complexity, invalidating results when a deadline is missed. Tracing is usually the most accurate and reliable tool to study the runtime behaviour of those applications. However, the interpretation of voluminous detailed execution traces requires a deep understanding of the operating system and application behaviour, and time to dig through the millions of trace events.In this paper, we present the use of model‐based constraints on top of user‐space and kernel traces to provide weighted analysis results. Our algorithms have been applied to multiple traces showing common problems for multi‐core real‐time systems. The experimental results show that our algorithms can quickly identify many different types of problems with a low runtime, even for traces with millions of events, thus helping to save time when analyzing thousands of trace events for complex systems.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".