Can complexity, coupling, and cohesion metrics be used as early indicators of vulnerabilities?
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.
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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 | 0.006 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".