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Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concerns

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arxiv 2405.06371 v2 pith:LIFMME2N submitted 2024-05-10 cs.CR cs.SE

classification cs.CRcs.SE
keywords softwaresecuritydevelopmentassistantscodeprofessionalstasksconcerns
verification ladder T0 review T1 audit T2 compute T3 formal
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Following the recent release of AI assistants, such as OpenAI's ChatGPT and GitHub Copilot, the software industry quickly utilized these tools for software development tasks, e.g., generating code or consulting AI for advice. While recent research has demonstrated that AI-generated code can contain security issues, how software professionals balance AI assistant usage and security remains unclear. This paper investigates how software professionals use AI assistants in secure software development, what security implications and considerations arise, and what impact they foresee on secure software development. We conducted 27 semi-structured interviews with software professionals, including software engineers, team leads, and security testers. We also reviewed 190 relevant Reddit posts and comments to gain insights into the current discourse surrounding AI assistants for software development. Our analysis of the interviews and Reddit posts finds that despite many security and quality concerns, participants widely use AI assistants for security-critical tasks, e.g., code generation, threat modeling, and vulnerability detection. Their overall mistrust leads to checking AI suggestions in similar ways to human code, although they expect improvements and, therefore, a heavier use for security tasks in the future. We conclude with recommendations for software professionals to critically check AI suggestions, AI creators to improve suggestion security and capabilities for ethical security tasks, and academic researchers to consider general-purpose AI in software development.

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  1. The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Programming Tasks

    cs.SE 2025-06 conditional novelty 6.0 of 10

    GitHub Copilot made undergraduate students faster and more test-successful on brownfield programming tasks, and shifted their workflow from manual coding and web search to prompting, reviewing, and integrating AI suggestions.

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