DreamCoder alternated symbolic program synthesis, library abstraction, and neural training so that solving tasks could gradually produce a better reusable language for future synthesis.
DeepCoder combined neural prediction with classical program search, showing an early way for deep learning to guide synthesis from input-output examples without generating complete source code directly.
Salesforce's CodeGen released open code models up to 16.1 billion parameters and explored multi-turn program synthesis, framing coding as a conversation that could decompose a larger intent into successive prompts.
Years before large language models generated code from natural-language prompts, programming-by-example systems such as Flash Fill synthesized small programs from demonstrations of the desired result.