Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects

Researchers evaluated Cursor, an AI-powered IDE, on its ability to generate large-scale software projects and found it achieves 91% functional correctness but produces significant design issues including code duplication, complexity violations, and framework best-practice breaches that threaten long-term maintainability.

This research reveals a critical gap between AI coding tools' functional capabilities and software quality standards. While Cursor demonstrated impressive performance in generating fully functional 16,000+ line projects with an average correctness score of 91%, the analysis uncovered over 4,400 design issues across the generated codebases. The study employed a Feature-Driven Human-In-The-Loop framework to systematically guide project generation, suggesting that even with structured human oversight, AI tools struggle with architectural principles and design patterns.

The prevalence of issues like code duplication, excessive method length, and violation of SOLID principles indicates that AI code generation currently optimizes for immediate functionality rather than maintainability and evolvability. These findings emerge as AI-powered development tools gain traction in enterprise environments, where long-term code quality directly impacts development velocity and technical debt accumulation.

For development teams and enterprises considering AI IDEs as productivity tools, these results highlight the necessity for experienced developer review and refactoring phases in any AI-generated codebase. The research demonstrates that AI tools cannot yet be trusted to produce production-ready code without human intervention beyond feature validation. This creates both a limitation and an opportunity: teams adopting these tools must establish quality gates and architectural review processes, while tool developers have clear targets for improvement in their training models and generation algorithms.

• →Cursor generates functionally correct large-scale projects at 91% accuracy but contains 4,400+ design quality issues requiring developer remediation
• →Most prevalent issues include code duplication, high complexity, large methods, and violations of design principles like DRY and SRP
• →AI-generated code requires experienced developer review despite functional correctness, creating additional quality assurance overhead
• →The Feature-Driven Human-In-The-Loop framework shows structured guidance improves project generation but doesn't eliminate architectural problems
• →Enterprise adoption of AI IDEs demands established code quality gates and architectural review processes to manage technical debt risks