Supervised Java To Javascript Code-To-Code Translator With Relative Position Encoding Attention Mechanism

Abstract

"This research aims to develop a transformer-based model or code-to-code translation, specifically from Java to JavaScript. The project will focus on the application of machine learning and natural language techniques to enable the automatic conversion of code to code from one programming language to another. The transformer-based graphcodebert model, a cutting-edge neural network architecture that has demonstrated outstanding performance in a variety of natural language processing applications, notably for code translation, will be used in this study. To enhance the positioning information of the structures of code snippets, the author used a relative positional encoding attention method to this model. This project will

utilize a dataset consisting of a parallel corpus of Java and JavaScript introduced by MuST- CoST / XLCost. The project’s success and evaluation will be checked using the BLEU scores

optimized for code translation. The outcome of the code translation will ease the developers who are trying to translate a source code written in Java code to JavaScript."