Name
Abstract | ||
|---|---|---|
We propose a framework to detect software bugs based on code pattern detection. Our framework will mine and generate bug patterns, detect those patterns in code, and calculate a vulnerability measure of software. While our framework performs well, we realize that it requires heavy manual tasks that render the framework infeasible to use in practice. However, we believe that recent advancements in machine learning will allow us to apply deep learning techniques to source code, which will help automate our framework for better practicality in the real world.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3314058.3317733 | Proceedings of the 6th Annual Symposium on Hot Topics in the Science of Security |
Field | DocType | ISBN |
Computer science,Artificial intelligence,Deep learning,Machine learning | Conference | 978-1-4503-7147-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Heaps | 1 | 1 | 2.09 |
| Xueling Zhang | 2 | 2 | 1.39 |
| Xiaoyin Wang | 3 | 749 | 29.19 |
| Travis D. Breaux | 4 | 655 | 47.75 |
| Jianwei Niu | 5 | 275 | 26.61 |