Survey of 162 vibe coders finds perceptions of AI code quality similar across experience levels but motivations, interaction styles, and quality assurance practices diverge, revealing a perception-action gap.
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Fine-tuning and prompting reduce some CWEs in AI-generated code but frequently introduce new weaknesses, with no strategy working reliably across models or languages.
AI IDEs with structured guidance can produce functional large-scale code but frequently introduce design flaws such as duplication, complexity, and principle violations that risk long-term maintainability.
citing papers explorer
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From Prompting to Verification: How Experience Shapes Vibe Coding Practices
Survey of 162 vibe coders finds perceptions of AI code quality similar across experience levels but motivations, interaction styles, and quality assurance practices diverge, revealing a perception-action gap.
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On Fixing Insecure AI-Generated Code through Model Fine-Tuning and Prompting Strategies
Fine-tuning and prompting reduce some CWEs in AI-generated code but frequently introduce new weaknesses, with no strategy working reliably across models or languages.
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Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects
AI IDEs with structured guidance can produce functional large-scale code but frequently introduce design flaws such as duplication, complexity, and principle violations that risk long-term maintainability.