Optimizing Software Supply Chain Vulnerability Mining and Remediation Paths Using Deep Learning Techniques
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
This paper constructs an overall framework for vulnerability mining, covering the whole process from code collection to vulnerability remediation.The word vector technique is used to transform code fragments into vector form, thus preserving the semantic information of the code.A vulnerability mining system based on semantic graph of source code is further designed, which generates a semantic graph of code by constructing an abstract syntax tree (SAT), and analyzes the semantic graph by using graph neural network to accurately locate potential vulnerabilities.At the same time, a vulnerability repair method based on thought chain is proposed.The results show that the model in this paper can accurately mine the vulnerabilities of web service software, and it consumes short latency and has strong stability.The results of web service software vulnerability detection show that the accuracy rate of the model always stays above 85% under different network structures.In addition, this paper obtains that the integration degree centrality measure and 60 iteration rounds have the best effect on the detection of vulnerabilities of the model.Finally, the vulnerability repair experiments show that at Beams=15, the model in this paper repairs each vulnerability function with a PPP metric of 61.52% and an average time of 3.168 seconds, which is the best for vulnerability repair.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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