Problem
Research questions and friction points this paper is trying to address.
Large Language Models
Secure Software Engineering Education
ChatGPT Usage
Cybersecurity Expertise
Vulnerability Fixing
Exploring the Role of Security Experience and ChatGPT Usage Strategies on Secure Software Engineering Education
🤖 AI Summary
研究探讨了26名网络安全硕士生在修复漏洞作业中使用ChatGPT的策略，发现使用的多样性与成绩正相关，为安全软件工程教育中有效利用大语言模型提供指导。
📝 Abstract
The rapid adoption of Large Language Models (LLMs) is reshaping software engineering education, but their role in secure software engineering education remains underexplored. We report an exploratory empirical study of how 26 graduate students in a part-time MSc Cybersecurity programme used ChatGPT during a vulnerability-fixing assignment. To characterise ChatGPT use, we analysed students' ChatGPT interaction logs using a structured double-coding procedure and examined whether usage patterns and prior cybersecurity expertise were associated with assignment performance. The results show that students with varying levels of cybersecurity expertise used broadly similar ChatGPT strategies. Individual usage patterns showed descriptive differences by grade, but none remained statistically significant after correcting for multiple comparisons. In contrast, diversity of ChatGPT usage, i.e., the number of distinct usage patterns adopted, was positively associated with performance, even after controlling for cybersecurity expertise. These exploratory findings suggest that the way students engage with ChatGPT may be more informative than whether they use it, and motivate future controlled studies to guide students toward effective LLM use in secure software engineering education.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Diversity of ChatGPT usage
Secure software engineering education
Large Language Models (LLMs)
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