Practical Uses of Formal Methods in Development of Airborne Software
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Over the past few decades, advanced methods have been developed for the analysis of digital systems using mathematical reasoning, i.e., formal logic. These methods are supported by sophisticated software tools that can be used to perform analysis far beyond what is practically achievable using “paper and pencil” analysis. In December 2011, RTCA published RTCA DO-178C [<span class="xref">1</span>] along with a set of technical supplements including RTCA DO-333 [<span class="xref">2</span>] which provides guidance on the use of formal methods towards the certification of airborne software. Such methods have the potential to reduce the cost of verification by using formal analysis instead of conventional test-based methods to produce a portion of the verification evidence required for certification. Formal methods can also be used to find problems earlier in the development process - for example, while the requirements are being developed rather than during system integration when the cost of re-working the requirements and design is much higher. This paper provides an introduction to the practical use of formal methods in the development and certification of airborne software. The paper includes an illustrative example of using formal methods motivated by experience with the application of formal methods to engine control software.</div></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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".