Paper Contents
Abstract
By improving problem detection and automating code creation, generative AI techniques have drastically changed software development. Large language model (LLM)-powered tools now help developers finish tasks, anticipate code topologies, and find errors instantly, saving manual labor and enhancing code quality. The performance of five popular AI-powered coding assistantsGitHub Copilot Enterprise, Amazon CodeWhisperer Pro, Codeium Pro, Cursor AI, and Tabnine Enterprisein terms of code completion and error detection is compared in this study. Contextual accuracy, IDE integration, language support, and security capabilities are the four main criteria we use to evaluate each tool, drawing on feature-based research and contemporary literature. The results show that although all tools help developers be more productive, their relative efficacy differs based on task difficulty, programming language. The results show that while all tools help developers be more productive, the relative efficacy of each tool differs based on the development environment, task complexity, and programming language. Notably, while some solutions provide better contextual comprehension or syntax-level recommendations, others excel at business customization and vulnerability detection. Based on operational requirements, security priorities, and project scope, this study attempts to aid developers, instructors, and organizations in choosing the best AI assistant.
Copyright
Copyright © 2025 Dr. P. Bastin Thiyagaraj. This is an open access article distributed under the Creative Commons Attribution License.
