Inductive Programming: A Survey of Program Synthesis Techniques

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Programming with a Differentiable Forth Interpreter (2016, background): “Inductive Programming approaches [12] aimed at inducing programs from incomplete speciﬁcations of the code to be implemented.”
Precise Condition Synthesis for Program Repair (2016, background): “This is known as preference bias in inductive program synthesis [15].”
Demystifying GPT Self-Repair for Code Generation (2023, background): “Our work is also different in that we assume access to the full collection of input-output examples, as is typically done in inductive synthesis [6, 10, 12, 16, 30].”
Synthesizing transformations on hierarchically structured data (2016, unknown): “We build on the tradition of inductive programming [18, 20, 22], where the goal is to synthesize functional or logic programs from examples.”
Compositional Explanations of Neurons (2020, background): “Now we search for the best logical form L ∈ L ( C ) : The arg max in Equation 3 ranges over a structured space of compositional expressions, and has the form of an inductive program synthesis problem [23].”
Synthesizing data structure transformations from input-output examples (2015, unknown): “Specifically, we build on the tradition of inductive programming [15, 18, 20], where the goal is to synthesize functional or logic programs from examples.”