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Research map / Image generation / Autoregressive and latent generation

Neural Program Generation Modulo Static Analysis

NeurIPS 2021 · Image generation › Autoregressive and latent generation · approach: Synthetic training data from generators · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Condition method-body generation on static-analysis attributesNSG versus CodeGPT · Java method generationPass all checks: 67.73% -> 86.41%Helped
Condition method-body generation on static-analysis attributes (ablation)NSG versus CNG · Java method generationPass all checks: 12.87% -> 86.41%Helped
Condition method-body generation on static-analysis attributesNSG versus GPTNeo1.3B · Java method generationSet of API Calls: 37% -> 53%Helped
Condition method-body generation on static-analysis attributes (ablation)NSG versus CNG · Java method generationSequences of Program Paths: 17% -> 39%Helped
Condition method-body generation on static-analysis attributesNSG versus GPTNeo1.3B · Java method generationAST Exact Match: 15% -> 26%Helped
Condition method-body generation on static-analysis attributesNSG versus Codex · Java method generationNo type errors: 91.42% -> 97.08%Helped

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