In-house Testing of Highly Hardware-dependent Software
Bibliographic record
Abstract
<div class="htmlview paragraph">In modern automotive systems, the complexity is growing by incorporating highly sophisticated microcontrollers running with more than 100MHz and consisting of more than 2500 registers. Software complexity is also growing in a similar, if not higher, rate.</div> <div class="htmlview paragraph">As semiconductor suppliers are also expected by their customers to include a hardware-dependent software layer in their products, testing must now include not only the hardware product but the delivery bundle of hardware and software modules.</div> <div class="htmlview paragraph">Testing this hardware-dependent software is complicated by the big amount of possible hardware-dependent configurations for this software layer which also extensively change the hardware test bench used in verification. The challenge of configuration complexity is solved by extensive use of test automation for both software generation, test bench configuration and test case runs. Additionally, in order to test the full delivery, all software modules have to be configured and tested together in a system-like test.</div> <div class="htmlview paragraph">This paper will discuss the Infineon approach in testing the microcontroller abstraction layer (MCAL) of the AUTOSAR implementation on 32-bit platforms.</div>
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".