This research provides the first empirical analysis of code repetition issues in 19 state-of-the-art large language models for code generation.

• Identifies prevalence and patterns of redundant code generation across multiple benchmarks
• Examines how model architecture, size, and training methods influence repetition tendencies
• Proposes techniques to mitigate repetition problems in LLM-generated code

For engineering teams, this research offers crucial insights for improving code quality, efficiency, and readability when leveraging AI coding assistants in production environments.

Code Copycat Conundrum: Demystifying Repetition in LLM-based Code Generation