The task of Programming Knowledge Tracing (PKT) is to predict student next programming performance using their history interactions with questions. Though rich features have been utilized in PKT such as code text, most existing methods only consider temporal features in student answer sequences. However, actual student programming process are more complex. In this work, we propose Interactive Programming Knowledge Tracing (IPKT), which uses an Interactive Memory Network (IMN) combined with Adversarial Training (AT) to simulate the learning process of reviewing question. Firstly, we design the IMN which learns the relation between knowledge state with answer information as the PKT backbone. Meanwhile, we predict the performance of the next question by fully connected network and use AT to enhance model's generalization of the question recognition. Extensive experiments are conducted on two real-world datasets and the experimental results demonstrate the effectiveness of our proposed model.