On Selecting Appropriate Development Processes and Requirements Engineering Methods for Secure Software
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.
Bibliographic record
Abstract
To avoid security vulnerabilities, there are many secure software development efforts in the directions of secure software development life cycle processes, security specification languages, and security requirements engineering processes. In this paper, we compare and contrast various secure software development processes based on a number of characteristics that such processes should have. We also analyze security specification languages with respect to desirable properties of such languages. Furthermore, we identify activities that should be performed in a security requirements engineering process to derive comprehensive security requirements. We compare different security requirements engineering processes based on these activities. Our analysis shows that many of the secure software requirements engineering methods lack some of the desired properties. The comparative study presented in this paper will provide guidelines to software developers for selecting specific methods that will fulfill their needs in building secure software applications.
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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 | 0.001 |
| 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 it