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Imagine a developer who can only change their last line of code, how oftenwould they have to start writing a function from scratch before it is correct?Auto-regressive models for code generation from natural language have a similarlimitation: they do not easily allow reconsidering earlier tokens generated. Weintroduce CodeFusion, a pre-trained diffusion code generation model thataddresses this limitation by iteratively denoising a complete programconditioned on the encoded natural language. We evaluate CodeFusion on the taskof natural language to code generation for Bash, Python, and Microsoft Excelconditional formatting (CF) rules. Experiments show that CodeFusion (75Mparameters) performs on par with state-of-the-art auto-regressive systems(350M-175B parameters) in top-1 accuracy and outperforms them in top-3 andtop-5 accuracy due to its better balance in diversity versus quality.

CodeFusion: A Pre-trained Diffusion Model for Code Generation