Detection of Common Problems in Real-Time and Multicore Systems Using Model-Based Constraints
Bibliographic record
Abstract
Multicore systems are complex in that multiple processes are running concurrently and can interfere with each other. Real-time systems add on top of that time constraints, making results invalid as soon as a deadline has been missed. Tracing is often the most reliable and accurate tool available to study and understand those systems. However, tracing requires that users understand the kernel events and their meaning. It is therefore not very accessible. Using modeling to generate source code or represent applications’ workflow is handy for developers and has emerged as part of the model-driven development methodology. In this paper, we propose a new approach to system analysis using model-based constraints, on top of userspace and kernel traces. We introduce the constraints representation and how traces can be used to follow the application’s workflow and check the constraints we set on the model. We then present a number of common problems that we encountered in real-time and multicore systems and describe how our model-based constraints could have helped to save time by automatically identifying the unwanted behavior.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".