SD Elements: A Tool for Secure Application Development Management.
Bibliographic record
Abstract
Abstract. A major problem in achieving security goals in application developmentis theoverwhelmingamount ofsecurity-relatedinformation, variety of tools, and numeroussecurity risks and vulnerabilities. Software analysts, developers, and testers are not often able to identify relevant security knowledge. Many security tools focus only on detecting vulnerabilities, but the embedded available security guidelines are usually not directly auditable. To fill these gaps, we introduce a new tool, called SD Elements, which focuses on prevention of vulnerabilities as opposed to detection. SD Elements is a centralized security knowledge base that covers different development life cycle phases, so security is built into the application from the early phases of the life cycle. Users are able to specify technologies, platforms, requirements, and programming languages, and SD Elements tailor security guidelines for different projects according to the user specifications. It enables businesses to provide tangible security audit evidence and trace compliance with security standards. The tool is currently being beta tested in varieties of firms, by different roles, and in different development phases. Keywords: Applicationsecurity,securityrequirements,developmentguidelines, security knowledge, test case. 1
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.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".