Model-level, platform-independent debugging in the context of the model-driven development of real-time systems
Bibliographic record
Abstract
Providing proper support for debugging models at model-level is one of the main barriers to a broader adoption of Model Driven Development (MDD). In this paper, we focus on the use of MDD for the development of real-time embedded systems (RTE). We introduce a new platform-independent approach to implement model-level debuggers. We describe how to realize support for model-level debugging entirely in terms of the modeling language and show how to implement this support in terms of a model-to-model transformation. Key advantages of the approach over existing work are that (1) it does not require a program debugger for the code generated from the model, and that (2) any changes to, e.g., the code generator, the target language, or the hardware platform leave the debugger completely unaffected. We also describe an implementation of the approach in the context of Papyrus-RT, an open source MDD tool based on the modeling language UML-RT. We summarize the results of the use of our model-based debugger on several use cases to determine its overhead in terms of size and performance. Despite being a prototype, the performance overhead is in the order of microseconds, while the size overhead is comparable with that of GDB, the GNU Debugger.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".