A Social Ontology for Integrating Security and Software Engineering
Bibliographic record
Abstract
As software becomes more and more entrenched in everyday life in today’s society, security looms large as an unsolved problem. Despite advances in security mecha-nisms and technologies, most software systems in the world remain precarious and vulnerable. There is now widespread recognition that security cannot be achieved by technology alone. All software systems are ultimately embedded in some human social environment. The effectiveness of the system depends very much on the forces in that environment. Yet there are few systematic techniques for treating the social context of security together with technical system design in an integral way. In this chapter, we argue that a social ontology at the core of a requirements engineering process can be the basis for integrating security into a requirements driven software engineering process. We describe the i* agent-oriented modelling framework and show how it can be used to model and reason about security concerns and responses. A smart card example is used to illustrate. Future directions for a social paradigm for security and software engineering are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".