Several advances in deep learning have been successfully applied to the software development process. Of recent interest is the use of neural language models to build tools, such as Copilot, that assist in writing code. In this paper we perform a comparative empirical analysis of Copilot-generated code from a security perspective. The aim of this study is to determine if Copilot is as bad as human developers. We investigate whether Copilot is just as likely to introduce the same software vulnerabilities as human developers. Using a dataset of C/C++ vulnerabilities, we prompt Copilot to generate suggestions in scenarios that led to the introduction of vulnerabilities by human developers. The suggestions are inspected and categorized in a 2-stage process based on whether the original vulnerability or fix is reintroduced. We find that Copilot replicates the original vulnerable code about 33% of the time while replicating the fixed code at a 25% rate. However this behaviour is not consistent: Copilot is more likely to introduce some types of vulnerabilities than others and is also more likely to generate vulnerable code in response to prompts that correspond to older vulnerabilities. Overall, given that in a significant number of cases it did not replicate the vulnerabilities previously introduced by human developers, we conclude that Copilot, despite performing differently across various vulnerability types, is not as bad as human developers at introducing vulnerabilities in code.
Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study
Centre for Research on Engineering Software Technologies (CREST) & Adelaide University, Australia（工程软件研究技术中心（CREST）及阿德莱德大学，澳大利亚）；Edith Cowan University, Australia（埃德温·考文大学，澳大利亚）；CSIRO’s Data61, Australia（CSIRO数据61，澳大利亚）
