In computer software, program defects are inevitable and are highly likely to cause significant losses. Therefore, it is a common consensus in academia and industry to find and eliminate potential defects in the program as early as possible. Most of the current automatic program repair methods follow the process of defect location, candidate generation, candidate verification. However, when the program is repaired, there is a problem that the repair rate is low and the repair result cannot be guaranteed. This study proposes a method for automatic repair of defects in C/C++ program based on program synthesis. Firstly, the error mode and its corresponding repair methods are summarized from the assembly that satisfies the same specification, and use the rewrite rules to express the error mode and its corresponding repair methods. On this basis, a defect-location method is implemented based on rewrite rules and program spectrum to obtain possible defect locations in the program. Secondly, the candidate-generation method is used to get the repair candidate based on the rewrite rule. At the same time, the correct structure of the program through deep learning is learnt to help predict the correct sentence structure of the wrong program error point. These two ways improve the quality of the candidate and the repair rate. Finally, in the candidate-verification process, the method of program synthesis is used. The sample program is used as a constraint to ensure the correctness of the synthesized code. Based on the above methods, the prototype tool AutoGrader is implemented and it is experimented on student program. The experimental results show that the proposed method has a high repair rate for the defects in the student program, and also ensures the correctness of the code after the repair.