Will the ICDs Please Stand Up? An Attempt to Reason about Subsystem Interfaces in Avionics System Integration
Bibliographic record
Abstract
System integrators for commercial aircraft reason about the subsystems they must integrate using Interface Control Documents (ICDs). Although ICD format standards exist in this specific domain, avionics suppliers express their ICDs in different ways, making the integrator's work challenging. As software engineering researchers, we are trying to define a common vocabulary for ICDs in the context of avionics systems integration. This paper describes this effort and the associated challenges. Our experience is that concepts such as architectural interfaces and abstraction, among others, are challenged in an avionics context. Moreover, understanding the concept of ICD, coming from a software engineering perspective, is not easy. Examples of ICDs in avionics are few, as real ICDs convey proprietary information which aircraft builders and their suppliers do not share publicly. We share our experience by describing an attempt to model ICDs for subsystems of a small avionics system, based on open standards, using both federated and IMA architectures. Our attempts lead us to conclude that it is required to model subsystems to a certain level of detail before establishing what their ICDs are (i.e. a bottom-up approach). The Architecture Analysis and Design Language (AADL) appears to be an appropriate language for this undertaking.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".