Bibliographic record
Abstract
It has been reported in the literature that about twenty new software vulnerabilities are reported weekly. This situation has increased the security awareness in the software community. Nowadays, software services are expected not only to satisfy functional requirements but also to resist malicious attacks. As demand for more trustworthy systems is increasing, the software industry is adjusting itself to security standards and practices by increasing security assessment and testing effort. Even though there is a consensus that better software engineering is to improve software quality in the early stage of software development, so far, various approaches that have been proposed to analyze and quantitatively measure the software security target, primarily show the finished software products in their operational life. There are few achievements on how to reduce or effectively mitigate the security risks faced by software products during the development process. In this chapter, the authors introduce a novel model-driven perspective on secure software engineering, which integrates seamlessly software security analysis with traditional software development activities. A systematic security engineering process that starts in the early stages of the software development process and spans the entire software lifecycle is presented. Fundamental software security concepts and analysis techniques are also introduced, and several illustrative examples are presented, with focus on security requirements and risk analysis.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".