Source Code Summarization refers to the task of creating understandable natural language summaries from a given code snippet. Good-quality and precise source code summaries are needed by numerous companies for a platitude of reasons - training for newly joined employees, understanding what a newly imported project does, in brief, maintaining precise summaries on the evolution of source code (using git history), categorizing the code, retrieving the code, automatically generating documents, etc. There is a considerable distinction between source code and natural language since source code is organized, has loops, conditions, structures, classes, and so on. Most of the models follow an encoder-decoder structure, we propose an alternative approach that uses UAST(Universal Abstract Syntax Tree) of the source code to generate tokens and then use the Transformer model for a self-attention mechanism which unlike the RNN method is helpful for capturing long-range dependencies. We have considered Java code snippets for generating code summaries.
Multi-Agent based Sequence Algorithm for Detecting Plagiarism and Clones in Java Source Code using Abstract Syntax Tree
