Empirical relation between coupling and attackability in software systems:
Bibliographic record
Abstract
Over the last decades, software quality attributes such as maintainability, reliability, and understandability have been widely studied. In contrast, less attention has been paid to the field of software security. Attackability is a concept proposed recently in the research literature t to measure the extent that a software system or service could be the target of successful attacks. Like most external attributes, attackability is to some extent disconnected from the internal of software products. To improve the quality of software products we need to be able to affect its internal features. So, for attackability measures to be useful for software products enhancement, we need to identify related internal software attributes. We study in this paper the empirical relationship between attackability as an external software quality attribute with coupling as an internal software attribute. Specifically, we use a case study based on denial of service (DOS) attacks conducted against a on line medical record keeping system. Through regression analysis, we establish that there is a strong correlation between attackability and coupling.
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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 | 0.001 | 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".