This research examines code defects generated by Large Language Models when dealing with real-world software development contexts and dependencies.

• LLMs produce non-syntactic mistakes that are harder to detect and potentially introduce security vulnerabilities
• Previous research focused mostly on standalone functions, overlooking context-dependent programming scenarios
• The study provides insights for improving code generation tools and preventing defects in automated programming
• Findings have significant implications for software engineering, security practices, and developer education

A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?