A Methodology for the Simplification of Tabular Designs in Model-Based Development
Bibliographic record
Abstract
Model-based development (MBD) is increasingly being used to develop embedded control software, with Matlab Simulink/Stateflow being the most widely used MBD language in the automotive industry. Stateflow truth tables, more traditionally known as decision tables, are often used for implementing complex decision-making logic. As the subsystems utilizing State flow truth tables evolve, they often grow more complex and become difficult to maintain and test. It is in part due to the nature of decision tables that makes them more difficult to check for desirable properties such as disjoint ness and completeness, resulting in reduced readability and scalability. Tabular expressions provide an alternative representation which does not suffer from many of the same problems. With the safety-critical nature of the automotive domain, as well as the continuous growth in both size and complexity of models, well-defined and principled methodologies are required for maintaining and refactoring tables. This paper presents a refactoring methodology for simplifying decision tables through the use of tabular expressions to facilitate testing, traceability and readability to help companies comply with ISO 26262. An automotive industrial case study is used to motivate the work and demonstrate the proposed methodology.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".