Inductive Program Synthesis by Using a Reversible Meta-Interpreter

Program synthesis by examples is more convenient than using conventional techniques, since it only requires examples instead of a detailed speci cation. In spite of this convenience, relatively little research in this type of synthesis has so far been carried out. A major obstacle is that, due to the similarity-based and data-driven features of this technique, many examples and considerable computation power are required to synthesize a complex program. To overcome this di culty, we propose that the learning of empirical synthesis processes be based on their explanations, in which the system explains how other similar programs are synthesized from given examples and transfers the explanation to the target. Since the programming know-how involved in the similar programs is already available, new programs are easily synthesized using only a small number of examples.