2026-08-14T12:05:38Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:24032 2025-12-12T14:58:30Z ddc:004 open_access

Enabling Secure Coding: Exploring GenAI for Developer Training and Education Amburi, Sathwik Espinha Gasiba, Tiago Lechner, Ulrike Pinto-Albuquerque, Maria Secure Coding Industry Software Development Generative AI Large Language Models Teaching The rapid adoption of GenAI for code generation presents unprecedented opportunities and significant security challenges. Raising awareness about secure coding is critical for preventing software vulnerabilities. To investigate how Generative AI can best support secure coding, we built an AI Secure Coding platform, an interactive training environment that embeds a GPT-4 based chatbot directly into a structured challenge workflow. The platform comprises a landing page, a challenges page with three AI-generated tasks, and a challenge page where participants work with code snippets. In each challenge, developers (1) identify vulnerabilities by reviewing code and adding comments, (2) ask the AI for help via a chat based interface, (3) review and refine comments based on AI feedback, and (4) fix vulnerabilities by submitting secure patches. The study involved 18 industry developers tackling three challenges. Participants used the AI Secure Coding Platform to detect and remediate vulnerabilities and then completed a survey to capture their opinions and comfort level with AI assisted platform for secure coding. Results show that AI assistance can boost productivity, reduce errors, and uncover more defects when treated as a "second pair of eyes," but it can also foster over-reliance. This study introduces the AI Secure Coding platform, presents preliminary results from a initial study, and shows that embedding GenAI into a structured secure-coding workflow can both enable and challenge developers. This work also opens the door to a new research field: leveraging GenAI to enable secure software development. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Sathwik Amburi and Tiago Espinha Gasiba and Ulrike Lechner and Maria Pinto-Albuquerque 2025 Is Part Of OASIcs, Volume 133, 6th International Computer Programming Education Conference (ICPEC 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.ICPEC.2025.2 urn:nbn:de:0030-drops-240321 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2025.2 eng https://creativecommons.org/licenses/by/4.0/legalcode