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Record W6997374744

Validation and Verification of Safety-Critical Systems in Avionics

2023· dissertation· en· W6997374744 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2023
Typedissertation
Languageen
FieldEngineering
TopicSafety Systems Engineering in Autonomy
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsComponent (thermodynamics)Reliability (semiconductor)InterchangeabilitySoftwareQuality (philosophy)Automatic test equipment
DOInot available

Abstract

fetched live from OpenAlex

This research addresses the issues of safety-critical systems verification and validation. Safety-critical systems such as avionics systems are complex embedded systems. They are composed of several hardware and software components whose integration requires verification and testing in compliance with the Radio Technical Commission for Aeronautics standards and their supplements (RTCA DO-178C). Avionics software requires certification before its deployment into an aircraft system, and testing is mandatory for certification. Until now, the avionics industry has relied on expensive manual testing. The industry is searching for better (quicker and less costly) solutions. 
\nThis research investigates formal verification and automatic test case generation approaches to enhance the quality of avionics software systems, ensure their conformity to the standard, and to provide artifacts that support their certification. 
\nThe contributions of this thesis are in model-based automatic test case generations approaches that satisfy MC/DC criterion, and bidirectional requirement traceability between low-level requirements (LLRs) and test cases. 
\nIn the first contribution, we integrate model-based verification of properties and automatic test case generation in a single framework. The system is modeled as an extended finite state machine model (EFSM) that supports both the verification of properties and automatic test case generation. The EFSM models the control and dataflow aspects of the system. For verification, we model the system and some properties and ensure that properties are correctly propagated to the implementation via mandatory testing. For testing, we extended an existing test case generation approach with MC/DC criterion to satisfy RTCA DO-178C requirements. Both local test cases for each component and global test cases for their integration are generated. The second contribution is a model checking-based approach for automatic test case generation. In the third contribution, we developed an EFSM-based approach that uses constraints solving to handle test case feasibility and addresses bidirectional requirements traceability between LLRs and test cases. Traceability elements are determined at a low-level of granularity, and then identified, linked to their source artifact, created, stored, and retrieved for several purposes. Requirements’ traceability has been extensively studied but not at the proposed low-level of granularity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.266
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it