Research on the requirement decomposition and test verification for vehicle data security
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
While data is considered as new production factors in various scenes of the automotive industry. The data interaction of vehicles also raises a significant amount of data security risks and issues. The vehicle development process needs to be considerate of satisfying data security requirements. The main objective of this paper is to investigate a method for decomposing vehicle data security requirements so that they can be met at different levels of the system during development. A principle of data grading is proposed in this paper, enabling the quantitative classification of vehicle data. Meanwhile, the process of test verification on basis of decomposing requirements is also the subject of this paper. Tests can be derived directly from the requirements at the finest level. The coverage analysis of requirements is explored integrating the impact of data classification, requirements decomposition and test results. This paper is useful for vehicle developers to conduct development and test verification for data security requirements.
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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 | 0.008 | 0.002 |
| 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 | 0.003 |
| 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