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Do Users Write More Insecure Code with AI Assistants?
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We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages. Overall, we find that participants who had access to an AI assistant based on OpenAI's codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities. Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants' language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.
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Cited by 1 Pith paper
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The Conversations Beneath the Code: Triadic Data for Long-Horizon Software Engineering Agents
Triadic data—synchronized human-human conversations, human-AI sessions, and cross-functional team work—is the essential substrate for training long-horizon software engineering agents.
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