Boosting component-based synthesis with control structure recommendation

Component-based synthesis is an important research field in program synthesis. API-based synthesis is a subfield of component-based synthesis, the component library of which are Java APIs. Unlike existing work in API-based synthesis that can only generate loop-free programs constituted by APIs, state-of-the-art work FrAngel can generate programs with control structures. However, for the generation of control structures, it samples different types of control structures all at random. Given the information about the desired method (such as method name and input/output types), experienced programmers can have an initial thought about the possible control structures that could be used in implementing the desired method. The knowledge about control structures in the method can be learned from high-quality projects. In this paper, we propose a novel approach of recommending control structures for API-based synthesis based on deep learning. A neural network that can jointly embed the natural language description, method name, and input/output types into high-dimensional vectors to predict the possible control structures of the desired method is proposed. We integrate the prediction model into the synthesizer to improve the efficiency of synthesis. We train our model on a codebase of high-quality Java projects from GitHub. The prediction results of the neural network are fed to the API-based synthesizer to guide the sampling process of control structures. The experimental results on 40 programming tasks show that our approach can effectively improve the efficiency of synthesis.