Bibliographic record
Abstract
Studying frequent code vulnerabilities in similar code, such as clones, near-duplicates, forked projects, or libraries, can help in the automated detection of security flaws during the software development process. In this work we conduct an empirical study on vulnerabilities in C/C++ code to characterize security flaws and find out if the same vulnerabilities exist in applications that share similar code or have the same business logic/domain. We analyze a code vulnerability dataset including 315 projects with 3284 security issues in 10,880 functions. Our results show that vulnerable functions in 35% of the most occurring CWEs (software weaknesses types) have similar code, and 23% of projects with the same domain/category have the same vulnerabilities. We observe that the most prevalent vulnerabilities in similar code are Use After Free, Improper Access Control, Cryptographic Issues, 7PK - Security Features, DoubleFree, Cross-site Scripting, and Divide By Zero. These vulnerabilities are, however, less frequent compared to other CWEs across all subjects. Our results suggest that automated vulnerability detection tools that work based on code similarity or abstract patterns can be tailored more towards certain CWEs.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| 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) | 0.001 | 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".