Bibliographic record
Abstract
The rapid development and expansion of network based applications have changed the computing world in the last decade. However, this overwhelming success has an Achilles’ heel: almost every software controlled system faces threats from potential adversaries both from internal and external users of the highly connected computing systems. These software systems must be engineered with reliable protection mechanisms, while still delivering the expected value of the software to their customers within the budgeted time and cost. The principal obstacle in achieving the above two different but interdependent objectives is that current software engineering processes do notprovide enough support for the software developers to achieve security goals. In this chapter, we reemphasize the principal objectives of both software engineering and security engineering, and strive to identify the major steps of a software security engineering process that will be useful for building secure software systems. Both software engineering and security engineering are ever evolving disciplines, and software security engineering is still in its infancy. This chapter proposes a unification of the process models of software engineering and security engineering in order to improve the steps of the software life cycle that would better address the underlying objectives of both engineering processes. This unification will facilitate the incorporation of the advancement of the features of one engineering process into the other. The chapter also provides a brief overview and survey of the current state of the art of software engineering and security engineering with respect to computer systems.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.024 |
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".