SimPact: Impact Analysis for Simulink 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
With the increasing use of Simulink modeling in embedded system development, there comes a need for effective techniques and tools to support managing these models and their related artifacts. Because maintenance of models makes up such a large portion of the cost and effort of the system as a whole, it is increasingly important to ensure that the process of managing models is as simple, intuitive and efficient as possible. Part of model management comes in the form of impact analysis - the ability to determine the impact of a change to a model on related artifacts such as test cases and other models. This paper presents an approach to impact analysis for Simulink models, and a tool to implement it (SimPact). We validate our tool as an impact predictor against the maintenance history of a large set of industrial models and their tests. The results show a high level of both precision and recall in predicting actual impact of model changes on tests.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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