On the Vulnerability of Large Corpora Source Code

被引：0
|
作者：
Barr, Joseph R. [1 ]
Thatcher, Tyler [1 ]
机构：
[1] Acronis SCS, Scottsdale, AZ 85251 USA
来源：
16TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2022) | 2022年
关键词：
Source Code; Android; OpenSSL; Linuxm Recurrent Neural Networks; LSTM; Accuracy; Perplexity; Out-of-Vocabulary; Byte-Pair Encoding; Big Data; Unbalanced data; Synthetic Sampling;
D O I：
10.1109/ICSC52841.2022.00058
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper is a part of a continual effort to score functions in source code for vulnerability. For practical reasons we've restricted our attention to the C and C++ programming languages. We demonstrate an auto-encoder network and techniques to embed source code into a low-dimensional Euclidean space and some of the issues encountered where dealing with a very large code base. We also describe a process of developing `code smell' features and a classifier when data is extremely unbalanced. Finally we explore how the workflow may generalize to other projects and programming languages.
引用
收藏
页码：314 / 317
页数：4
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