  Pseudo-code written by natural language is helpful for novice developers'
program comprehension. However, writing such pseudo-code is time-consuming and
laborious. Motivated by the research advancements of sequence-to-sequence
learning and code semantic learning, we propose a novel deep pseudo-code
generation method DeepPseudo via code feature extraction and Transformer. In
particular, DeepPseudo utilizes a Transformer encoder to perform encoding for
source code and then use a code feature extractor to learn the knowledge of
local features. Finally, it uses a pseudo-code generator to perform decoding,
which can generate the corresponding pseudo-code. We choose two corpora (i.e.,
Django and SPoC) from real-world large-scale projects as our empirical
subjects. We first compare DeepPseudo with seven state-of-the-art baselines
from pseudo-code generation and neural machine translation domains in terms of
four performance measures. Results show the competitiveness of DeepPseudo.
Moreover, we also analyze the rationality of the component settings in
DeepPseudo.
