Analysis of Machine Code Using Natural Language Processing

In this paper we have created a Machine Learning Model for Machine Languages, this field of processing machine language and converting/making meaningful iterations out of it comes under Natural Language Processing. Existing approaches on NLP were focused on human-to-human interactions i.e., English to French, German, Hindi. In this research we try to analyze lexical in machine code, break it down to smaller meaningful bits and make some inferences like the type of languages, number of loops, functionality, and time complexity of code, rewriting of code, converting code from one language to another. We have mainly focused on the recognition of the type of language by testing a small snippet of code. We have also focused on a bigger real-world problem which is conversion of code from one language to another with the same basic meaning and logic, just changing the syntax. We have deployed the machine learning model for public use, and open-sourced Flask API is deployed on the cloud for usage that accepts 3 types of machine languages and returns the type of syntax. We have created a simple Angular App for the demonstration and testing of the model which is hosted and is live online. link (https://nlpapp-8e2fd.web.app/)