Interplay of Human Factors and Secure Architecture Design using Model-Driven Engineering
Bibliographic record
Abstract
When developing a secure software architecture, a development team must collaborate to make critical security-related decisions. The human factors of the development team members play a vital role in secure architecture design and therefore must be considered when forming or evaluating development teams for a software project. In this paper, we present a model-driven approach for studying the interplay of human factors and secure architecture design. Specifically, we propose a conceptual model for considering direct and indirect human factors of the development team during secure software design and a set of modeling languages to represent the human factors. We also provide a questionnaire-based methodology to evaluate human factors of development team members and define team profiles. The approach enables characterizing the human factors of team members desired to achieve the protection goals of software architecture assets and to determine which team members should be participating in the decision-making for the design to achieve the goals for assets by matching the desired human factors to members belonging to team profiles. This approach can improve the confidence on the decision-making capabilities of teams when faced with critical security-related design designs. We illustrate the approach using a generic SCADA system use case.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".