A Preliminary Report on Tool Support and Methodology for Feature Interaction Detection
Bibliographic record
Abstract
In this report, we describe our effort to create in Matlab’s Stateflow a set of non-proprietary advanced automotive feature design models, and to translate these design models into models that can be input to the model checker SMV. We are interested in verifying the absence of feature interactions in the integration of the automotive features, as well as the lack of errors in the design. In the automotive domain, a feature is a bundle of system functionality recognized by the driver and providing advanced functionality to the vehicle, for instance, Cruise Control (CC). Each feature is normally implemented in software and has a degree of control over the mechanical components that operate the dynamics of the vehicle. Examples of these mechanical components are brakes, throttle and steering. We have created our set of non-proprietary feature design models to assess different techniques and tools that analyze the integration of the features and their correctness. The translated models will allow us to verify properties of the automotive features at the same level of description as the design, so we can ensure that the findings of our analysis are applicable to the design of the automotive components. 1
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".