The Sketching Approach to Program Synthesis

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How researchers see this paper

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Programmatically Interpretable Reinforcement Learning (2018, methodology): “One of the key ideas to make this search tractable is to provide the synthesizer a sketch of the desired program in addition to the examples, for example in (Solar-Lezama, 2009) and (Feser et al., 2015).”
Neurosymbolic Programming (2021, background): ““The Sketching Approach to Program Synthesis”.”
Code completion with statistical language models (2014, unknown): “Examples where partial programs are used heavily include the sketching approach [30] to program synthesis.”
Bayesian Sketch Learning for Program Synthesis (2017, background): “These constraints are imposed either by having an incomplete syntactic model, known as a program sketch [43, 44], as part of the problem instance, or by restricting synthesis to a narrowly tailored domain-specific language (DSL) [9, 19, 35].”
Semantic Scaffolds for Pseudocode-to-Code Generation (2020, background): “While these do not encode the full spectrum of constraints used in some formal program synthesis tools (Solar-Lezama, 2009; Gulwani et al., 2017), they strike a balance between utility, speed, and ease of use, offering substantial improvements in system performance without a significant increase in ”
Probabilistic programming (2014, background): “Having the programmer sketch [53] some outline of the program and having the system automatically fill in the details will greatly help improve usability.”