An Exploratory Study on the Relationship of Smells and Design Issues with Software Vulnerabilities
Bibliographic record
Abstract
Software vulnerabilities are a major concern for companies as they must ensure that their data is protected from security breaches. The latter can result in confidential and sensitive data being lost, corrupted, modified, or destroyed and, subsequently, have serious consequences such as financial loss and a damaged reputation for the company. Many software companies strive to practice secure coding practices as a preventive measure or bring potential vulnerabilities to light before the software is deployed. Traditionally, metrics have been widely used to uncover vulnerabilities. However, many studies have recently used code smells to disclose vulnerabilities. This preliminary study explores the relationship between smells, design issues, and software vulnerabilities. As smells and design issues are indicators of deeper problems in the software, establishing their relationship with vulnerabilities can be helpful for vulnerability prediction. We analyzed 561 versions of nine open-source software by exploring the smells and design issues in vulnerable and non-vulnerable classes. We found that a subset of smells and design issues have a statistically significant relationship with the vulnerable classes. On the other hand, after performing a manual analysis using the vulnerability descriptions and vulnerability fix-commit details, we found no indication that smells or design issues induce vulnerabilities. In addition, we found that smells and design issues were still present in those code segments even after resolving the vulnerabilities.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".