Generation of Pseudo Code from the Python Source Code using Rule-Based Machine Translation
This work addresses a domain-specific need for software developers by automating pseudo code generation, but it appears incremental as it builds on existing rule-based methods.
The paper tackles the problem of automatically generating pseudo code from Python source code to aid program comprehension, using a rule-based machine translation approach that converts Python to XML for information extraction and then applies templates, with initial performance results reported.
Pseudo code is one of the valuable artifacts to comprehending the complex program codes. Most of the source code still has no equivalent pseudo code, due to the time-consuming process of writing pseudo codes. In this work, we have developed an approach to generate pseudo code from the python source code. In the first step, we convert python code into XML code for better code information extraction. Next, Important information extracted from the XML code, which later on used to generate actual pseudo code with the help of pseudo code templates. Initial performance results have been discussed in this paper.