Fine-grained Pseudo-code Generation Method via Code Feature Extraction and Transformer

TL;DR

This paper introduces DeepPseudo, a deep learning approach combining code feature extraction and Transformer models to generate accurate pseudo-code from source code, aiding program comprehension.

Contribution

It presents a novel pseudo-code generation method that integrates code feature extraction with Transformer architecture, outperforming existing baselines on real-world datasets.

Findings

DeepPseudo outperforms seven state-of-the-art baselines.

The method effectively captures local code features.

Component analysis confirms the effectiveness of the design choices.

Abstract

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.