Representing Partial Programs with Blended Abstract Semantics
This work provides a more effective way to represent partial programs, which is crucial for improving the efficiency and capability of program synthesis engines for developers and researchers in the field.
The paper addresses the challenge of representing partially written programs during program synthesis to guide the search process. It introduces a technique that combines concrete execution state with learned neural representations, enabling synthesizers to handle more complex language constructs like loops and higher-order functions and achieve higher accuracy for a given search budget compared to pure neural approaches.
Synthesizing programs from examples requires searching over a vast, combinatorial space of possible programs. In this search process, a key challenge is representing the behavior of a partially written program before it can be executed, to judge if it is on the right track and predict where to search next. We introduce a general technique for representing partially written programs in a program synthesis engine. We take inspiration from the technique of abstract interpretation, in which an approximate execution model is used to determine if an unfinished program will eventually satisfy a goal specification. Here we learn an approximate execution model implemented as a modular neural network. By constructing compositional program representations that implicitly encode the interpretation semantics of the underlying programming language, we can represent partial programs using a flexible combination of concrete execution state and learned neural representations, using the learned approximate semantics when concrete semantics are not known (in unfinished parts of the program). We show that these hybrid neuro-symbolic representations enable execution-guided synthesizers to use more powerful language constructs, such as loops and higher-order functions, and can be used to synthesize programs more accurately for a given search budget than pure neural approaches in several domains.