Automatic software generation based on some specification is known as program synthesis. Most existing approaches formulate program synthesis as a search problem with discrete parameters. In this paper, we present a novel formulation of program synthesis as a continuous optimization problem and use a state-of-the-art evolutionary approach, known as Covariance Matrix Adaptation Evolution Strategy to solve the problem. We then propose a mapping scheme to convert the continuous formulation into actual programs. We compare our system, called GENESYS, with several recent program synthesis techniques (in both discrete and continuous domains) and show that GENESYS synthesizes more programs within a fixed time budget than those existing schemes. For example, for programs of length 10, GENESYS synthesizes 28% more programs than those existing schemes within the same time budget.

Program synthesis using inputs and outputs is a fundamental problem in computer science. Towards that end, we present a framework, called NetSyn, that synthesizes programs using an evolutionary algorithm. NetSyn makes several novel contributions. First, NetSyn uses neural networks as a fitness function. This addresses the principal challenge of evolutionary algorithm: how to design the most effective fitness function. Second, NetSyn combines an evolutionary algorithm with neighborhood search to expedite the convergence process. Third, NetSyn can support a variety of neural network fitness functions uniformly. We evaluated NetSyn to generate programs in a list-based domain specific language. We compared the proposed approach against a state-of-the-art approach to show that NetSyn performs better in synthesizing programs.