International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025
p-ISSN: 2395-0072
www.irjet.net
A Review of Development of an AI-Based Code Completion Tool for Enhancing Developer Productivity and Efficiency Neha Singh1, Deepshikha2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Artificial intelligence (AI) has rapidly
increased the pace of software development, and there are now indispensable tools like AI powered code completion tools that developers rely on. In short, machine learning, deep learning and natural language processing (NLP) are leveraged to make coding more efficient, reduce development time and increase code quality with these tools. This review paper analyzes AI based code completion tools, their underlying technologies and its influence on developer productivity. We review the most popular AI led solutions, OpenAI Codex (GitHub Copilot), Tabnine, and Amazon CodeWhisperer to discuss their features, pros and cons. Additionally, we address the problems faced by AI coding including accuracy, ethics and dependency on such tools from developers. Moreover, the paper also covers the upcoming trends that involve personalized code recommendations and AI augmented pair programming for intelligent software development in the future. This review synthesized current research and industry progress to provide an inventory into the advancement of the use of AI based code completion, the potential and challenges, for future innovations in this area.
Figure-1: AI tools. AI driven code completion reduces cognitive load of programmers and errors which further increases the productivity and efficiency. Given the growing amount of AI tools being adopted in software development, it is important to understand the evolution, effectiveness, and possible of such tools.
1.2 Objectives of the Review
Key Words: AI-based code completion, software development, developer productivity, machine learning, deep learning, natural language processing, GitHub.
This review aims to:
Analyze the development and advancements in AIbased code completion tools.
Explore the key technologies enabling intelligent code suggestions, such as deep learning models and NLP techniques.
Compare and evaluate existing AI-driven code completion tools in terms of accuracy, usability, and performance.
Assess the impact of AI-assisted coding on developer productivity and efficiency.
Identify challenges and limitations of AI-based code completion, including ethical concerns and technical constraints.
Discuss future trends and research directions in AIdriven software development.
1. INTRODUCTION 1.1 Background and Motivation Over the recent few year, the Software development field transformed due to the rising need for complex, scalable and high quality applications. However, as software systems continue to increase in complexity, this has become a difficult task especially when it comes to writing large volumes of code, ensuring accuracy, and maintaining their efficiency. While traditional coding methods like manual typing and rule based autocompletion go a fair way, they lack in resolving challenges of today and more. Addressing these challenges, the revolutionary solution developed are AI based code completion tools. These tools use machine learning techniques, such as deep learning and natural language processing along with these techniques provide assistance to developers by predicting and suggesting relevant code snippet in real time.
© 2025, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 794