The experiments show promising performance results compared to other language models of machine translation. The proposed model is evaluated using Django and SPoC datasets. Third, the replacement process is performed to obtain the target pseudo code. Second, pass the source code retrieved (input retrieved) to the deep learning model based on the transformer to generate the pseudocode retrieved. First, we retrieve the sentences that are similar to the input sentence using different similarity methods. The proposed model handles words of low frequency and words that do not exist in the training dataset. The proposed model adopts different retrieval similarity methods and neural machine translation to generate pseudocode.

In this paper, a novel retrieval-based transformer pseudocode generation model is proposed. Pseudocode explains and describes code contents that are based on the semantic analysis and understanding of the source code. The comprehension of source code is very difficult, especially if the programmer is not familiar with the programming language.

Automatic pseudocode writer