[2603.15298] The Impact of AI-Assisted Development on Software Security: A Study of Gemini and Developer Experience

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Computer Science > Software Engineering

arXiv:2603.15298 (cs)

[Submitted on 16 Mar 2026 (v1), last revised 17 Mar 2026 (this version, v2)]

Title:The Impact of AI-Assisted Development on Software Security: A Study of Gemini and Developer Experience

Authors:Nadine Jost, Benjamin Berens, Manuel Karl, Stefan Albert Horstmann, Martin Johns, Alena Naiakshina<br>View a PDF of the paper titled The Impact of AI-Assisted Development on Software Security: A Study of Gemini and Developer Experience, by Nadine Jost and 5 other authors

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Abstract:The ongoing shortage of skilled developers, particularly in security-critical software development, has led organizations to increasingly adopt AI-powered development tools to boost productivity and reduce reliance on limited human expertise. These tools, often based on large language models, aim to automate routine tasks and make secure software development more accessible and efficient. However, it remains unclear how developers' general programming and security-specific experience, and the type of AI tool used (free vs. paid) affect the security of the resulting software. Therefore, we conducted a quantitative programming study with software developers (n=159) exploring the impact of Google's AI tool Gemini on code security. Participants were assigned a security-related programming task using either no AI tools, the free version, or the paid version of Gemini. While we did not observe significant differences between using Gemini in terms of secure software development, programming experience significantly improved code security and cannot be fully substituted by Gemini.

Subjects:

Software Engineering (cs.SE); Human-Computer Interaction (cs.HC)

Cite as:<br>arXiv:2603.15298 [cs.SE]

(or<br>arXiv:2603.15298v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2603.15298

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arXiv-issued DOI via DataCite

Submission history<br>From: Nadine Jost [view email]<br>[v1]<br>Mon, 16 Mar 2026 13:59:06 UTC (441 KB)

[v2]<br>Tue, 17 Mar 2026 10:48:08 UTC (441 KB)

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