Abstract:
"
Source code summarization or comment generation is the task of producing readable natural
language annotations for the given source code which can be used to comprehend source code
with less effort. Software engineers use comments while programming to understand source
code but unfortunately writing comments remains a time-consuming task therefore there has
been substantial interest in automating the process by the software engineering and machine
learning community over the period. Even though there were several approaches they failed to
catch the source code’s long-term dependencies and the pairwise relationship between code
tokens therefore in this research a novel transformer-based hybrid modelling approach was
used to solve the issue of generating comments.
Auto Commenter system was built with the intention of generating accurate comments for
multiple programming languages. This dissertation act as proof for the success of the Auto
commentator in achieving its aim. Auto commenter uses a hybrid modelling mechanism to
learn different information of source code such as the syntactical and structural information.
The combined source code modelling along with the state of the art transformer architecture
which uses multiple attention mechanism and position-wise encoding gave a promising result
and made it a success above other available solutions."
