My Code Is Less Secure with Gen AI: Surveying Developers' Perceptions of the Impact of Code Generation Tools on Security
ACM/SIGAPP Symposium on Applied Computing (SAC), pp. 1637–1646
Abstract
Background: Generative AI (GAI) tools like GitHub Copilot and ChatGPT are transforming software development by automating code generation and enhancing developers' productivity. However, since these tools are often trained on open-source repositories, they may inadvertently reproduce vulnerable code, raising concerns about the security of AI-generated outputs. Aims: In this paper, we aim to investigate how developers perceive code security when using GAI tools. Method: We conducted a survey with 105 software developers with diverse experience levels to gather their perceptions regarding the security of generated code and their suggestions for improving it. Results: While developers reported increased development speed when using GAI tools, many spend additional time on security reviews and documentation of the generated code, and they are worried about the overreliance on AI and vulnerabilities in the code. Only about a quarter of the developers expressed confidence in the code generated by AI, and, moreover, experienced developers perceive that their proficiency in secure coding decreases when using GAI tools. Our results provide organizations with a better understanding of the risks associated with GAI tools and help improve their software security programs.
Authors 3
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Arina Kudriavtseva Aachen
Affiliation as printed
Leiden University, Leiden, Netherlands
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Nisar Ahmad Hotak Aachen
Affiliation as printed
Leiden University, Leiden, Netherlands
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Olga Gadyatskaya Aachen
Affiliation as printed
Leiden University, Leiden, Netherlands
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