John Heaps
Travis Breaux
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.
- Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 265--283.Google Scholar
Digital Library
- Nathaniel Ayewah, David Hovemeyer, J David Morgenthaler, John Penix, and William Pugh. 2008. Using static analysis to find bugs. IEEE software 25, 5 (2008). Google ScholarDigital Library
- Peter Mell, Karen Scarfone, and Sasha Romanosky. 2006. Common vulnerability scoring system. IEEE Security & Privacy 4, 6 (2006). Google ScholarDigital Library
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).Google Scholar
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