Generating Reliable Code from Hybrid-Systems Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hybrid systems have emerged as an appropriate formalism to model embedded systems as they capture the theme of continuous dynamics with discrete control. Under this paradigm, distributed embedded systems can be modeled as a network of communicating hybrid automata. Several techniques for code generation from these models have also been proposed and commercially implemented. Providing formal guarantees of the generated code with respect to the model, however, has turned out to be a hard problem. While the model is set in continuous time with concurrent execution and instantaneous switching, the code running on an inherently discrete platform, can be affected by the sampling interval, round-off errors, and communication delays between the sensor, controller, and actuators. Consequently, semantic differences between the model and its code can arise with potentially different system behavior. This paper proposes a criterion for faithful implementation of the hybrid-systems model with a focus on its switching semantics. We discuss different techniques to ensure a faithful implementation of the model, and test the feasibility of our concepts by implementing a model heater system. In this heater case study, we successfully eliminate all fault transitions and, thereby, generate code with correct behavior complying with the specification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it