An Automatic Code Generation Method Based on Sequence Generative Adversarial Network
Hanwen Sun, Yuanping Nie, Xiang Li, Minhuan Huang, Jianwen Tian, Wei Kong · 2022
Automatic code generation has been around for a long time, and the core technologies used are constantly evolving. Around application development work in the demand for high quality code generated rapidly, this paper proposes a automatic code generation method based on sequence generative adversarial network, the method adopts the adversarial learning thought, using LSTM as a generator, CNN as a discriminator, both through against each other to generate higher quality code samples. Experiment results show that this method can generate code automatically at the second level, and is significantly better than the existing methods in terms of generation rate and accuracy, which explores a new way of automatic code generation.