Can complexity, coupling, and cohesion metrics be used as early indicators of vulnerabilities?
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
It is difficult to detect vulnerabilities until they manifest themselves as security failures in the operational stage of software, because the security concerns are not addressed or known sufficiently early during software development. Complexity, coupling, and cohesion (CCC) related software metrics can be measured during the earlier phases of software development. If empirical relationships can be discovered between CCC metrics and vulnerabilities, these metrics could aid software developers to take proactive actions against potential vulnerabilities in software. In this paper, we conduct an extensive case study on Mozilla Firefox to provide empirical evidence on how vulnerabilities are related to complexity, coupling, and cohesion. We find that CCC metrics are correlated to vulnerabilities at a statistically significant level. We further examine the correlations to determine which level (design or code) of CCC metrics are better indicators of vulnerabilities. We also observe that the correlation patterns are stable across multiple releases of the software. These observations show that CCC metrics can be dependably used as early indicators of vulnerabilities in software.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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