Stateflow to Tabular Expressions
Bibliographic record
Abstract
Stateflow is a visual tool that is used extensively in industry for designing the reactive behaviour of embedded systems. Stateflow relies on techniques like simulation to aid the user in finding flaws in the model. However, simulation is inadequate as a means of detecting inconsistencies and incompleteness in the model. Tabular Expressions (function tables) have been used successfully in software development for more than thirty years. Tabular expressions are also visual representations of functions, but include the important properties of completeness and disjointness. In other words, a tabular expression is well-formed only when the input domain is covered completely (completeness), and when there is no ambiguity in the behaviour described by the tabular expression (disjointness). The goal of our work is to use the completeness and disjointness properties of well-formed tabular expressions to aid us in establishing those properties in Stateflow models. From the Stateflow models, we generate a new kind of tabular expression that includes extended output options. We use the informal Stateflow semantics from MathWorks documentation as the basis for generating our tabular expressions. The generated tabular expressions are then used to guarantee completeness and disjointness. We provide a transformation algorithm that we plan to implement in a tool to automatically generate tabular expressions from Stateflow models.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".