  Code summarization (CS) and code generation (CG) are two crucial tasks in the
field of automatic software development. Various neural network-based
approaches are proposed to solve these two tasks separately. However, there
exists a specific intuitive correlation between CS and CG, which have not been
exploited in previous work. In this paper, we apply the relations between two
tasks to improve the performance of both tasks. In other words, exploiting the
duality between the two tasks, we propose a dual training framework to train
the two tasks simultaneously. In this framework, we consider the dualities on
probability and attention weights, and design corresponding regularization
terms to constrain the duality. We evaluate our approach on two datasets
collected from GitHub, and experimental results show that our dual framework
can improve the performance of CS and CG tasks over baselines.
