CODE2VEC Based Cognitive Agent System to Retrieve Relevant Code Component from Repository

摘要

The cognitive agent system helps to retrieve most relevant code component by introducing latest techniques. In this paper the authors used latest approach of code embedding which undergoes code2vec tokenization model by tokenizing and converting the code components present in the dataset into a numeric representation to create a input for neural network environment and also implemented cosine similarity matching technique to acquire the relevancy and perform retrieval of code component.