Constrained Diffusion for Code (CDC) integrates constraint satisfaction into the reverse denoising process of discrete diffusion models via constraint-aware operators that use optimization and program analysis to steer generation toward feasible programs.
Díaz Ferreyra, Markus Mutas, Salem Dhiff, and Riccardo Scandariato
2 Pith papers cite this work. Polarity classification is still indexing.
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A synthesis of 30 secondary studies finds strong benchmark accuracy for LLM code generation but weak real-world generalization, fragile robustness, pervasive efficiency issues, and under-reported bias, calling for domain-aware improvements and standardized evaluation.
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Constrained Code Generation with Discrete Diffusion
Constrained Diffusion for Code (CDC) integrates constraint satisfaction into the reverse denoising process of discrete diffusion models via constraint-aware operators that use optimization and program analysis to steer generation toward feasible programs.
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A Tertiary Review of Large Language Model-Based Code Generating Tasks: Trends, Challenges, and Future Directions
A synthesis of 30 secondary studies finds strong benchmark accuracy for LLM code generation but weak real-world generalization, fragile robustness, pervasive efficiency issues, and under-reported bias, calling for domain-aware improvements and standardized evaluation.