A methodology for the simplification of tabular designs in model-based development
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
Model-based development (MBD) is an increasingly used approach for the development of embedded control software, with Matlab Simulink/Stateflow as the widely accepted language. The adoption of this development paradigm is prevalent in many safety-critical domains, including the automotive industry. With an increasing reliance on software for controlling vehicle functionality and the yearly advent of new vehicle features, automotive models have been growing in size and complexity, causing them to become increasingly difficult to maintain, refactor, and test. Given the centrality of models in MBD, it is a requisite that they be maintained under well-defined and principled software development processes that use precise notation to document system requirements and behavioural design description. Tabular methods have long been used for defining decision-making logic in software, due to their concise and precise manner of communicating complex behaviour, so it is not surprising that they are finding increased use in automotive software models. Thus their presence in Simulink models is increasingly prominent in the implementation of complex behaviour in production code. As a result of the safety-critical nature of the automotive industry, as well as the increasing size and complexity of its models, reliable refactoring and simplification techniques for tabular expressions are becoming an important need for automotive companies. To address this need, this thesis presents a methodology for refactoring complex tabular designs to improve requirements traceability with a focus on Matlab Simulink/Stateflow and the MBD approach. A case study of industrial examples from an automotive partner are used to motivate the work and demonstrate the proposed methodology's effectiveness in reducing design size and complexity, while also increasing testability and requirements traceability.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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