Schedulability analysis for automated implementations of real-time object-oriented models
Bibliographic record
Abstract
The increasing complexity of real time software has led to a recent trend in the use of high level modeling languages for development of real time software. One representative example is the modeling language ROOM (real time object oriented modeling), which provides features such as object orientation, state machine description of behaviors, formal semantics for executability of models, and possibility of automated code generation. However these modeling languages largely ignore the timeliness aspect of real time systems, and fail to provide any guidance for a designer to a priori predict and analyze temporal behavior. We consider schedulability analysis for automated implementations of ROOM models, based on the ObjecTime toolset. This work builds on results presented by M. Saksena (1997), where we developed some guidelines for the design and implementation of real time object oriented models. Using the guidelines, we have modified the run time system library provided by the ObjecTime toolset to make it amenable to schedulability analysis. Based on the modified toolset, we show how a ROOM model can be analyzed for schedulability, taking into account the implementation overheads and structure. The analysis is validated experimentally, first using simple periodic models, and then using a large case study of a train tilting system.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".