Enhancing Personalized Shopping Experiences in E-Commerce through Artificial Intelligence: Models, Algorithms, and Applications

Aakash Srivastava *, Writuraj Sarma and Sudarshan Prasad Nagavalli

Independent Researcher.
 
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2021, 03(02), 135-148.
Article DOI: 10.30574/wjaets.2021.3.2.0072
Publication history: 
Received on 22 September 2021; revised on 25 October 2021; accepted on 27 October 2021
 
Abstract: 
Code refactoring entails enhancing the current code readability, maintainability, and efficiency without changing its external behavior within a software development process. Traditional refactoring techniques which depended on human interventions or IDE-based tools are often strenuous, tedious, and prone to errors. Therefore, emerging factors like AI-related approaches, especially those entailing machine learning algorithms, have indicated promising alternatives which would alleviate such inherent challenges in manual refactoring processes by automating code refactoring. AI-enabled tools examine massive codebases, identify code smells, and recommend optimal refactoring approaches once learned from history and patterns. These tools automatically improve, hence adding value to the maintainability of software, reducing technical debt, and lowering manual intervention that was previously needed. Therefore, this paper explores how the artificial intelligence approach can be used to complement refactoring, underlining the different approaches that refactoring takes over traditional ones, while making commentary on the consequences of such technology in contemporary software engineering practice. As AI-enabled refactoring techniques continue to improve, they are likely to contribute a lot towards bettering software quality, enhancing developer productivity, and reducing software design faults in the near future. 
 
Keywords: 
Code Refactoring; Artificial Intelligence; Machine Learning; Software Maintainability; Code Smells; Automated Refactoring; Deep Learning; Software Optimization; Technical Debt; Software Engineering
 
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Copyright information: 
Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0