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Prompt Design and Feature Comparison in AI Coding Assistants
국제인공지능학회(구 한국인터넷방송통신학회) The International Journal of Advanced Smart Convergence Volume 14 Number 4 2025.12 pp.616-627
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
Large language models have catalyzed significant changes in software development through AI-powered coding assistants. This study examines prompt design strategies and features of three widely-used tools: GitHub Copilot, ChatGPT, and Amazon CodeWhisperer. Through systematic benchmarking using algorithmic problems, API integration tasks, and debugging scenarios, we quantify performance differences across accuracy (71-92%), executability (71-94%), and security vulnerability rates (3-12%). GitHub Copilot demonstrates strongest algorithmic performance (92%), while CodeWhisperer excels in API integration (88%) with lowest security vulnerability rate (3%). We provide evidence-based tool selection guidelines and a practical prompt engineering checklist for developers and educators.
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