Analysis and Evaluation of Tools, Libraries and Plug-Ins for Ai Powered Software Development

This research evaluates the impact of AI-powered libraries, plug-ins, and tools on software development based on their features, productivity, and integration problems. With GitHub Copilot, OpenAI Codex, and others like them coming into the market at a rapid rate, developers are able to automate mundane activities, increase coding productivity, and reduce cognitive load. The tools have limitations in dealing with sophisticated tasks, code correctness, and ethics.

The research uses a mixed-methods research approach with hands-on testing combined with developer input in measuring tools such as GitHub Copilot, Tabnine, Cody by Sourcegraph, and Amazon Q. The research uses performance metrics of time saved, code recommendation accuracy, and user satisfaction. The research also compares top-tier AI libraries such as TensorFlow, PyTorch, and Scikit-learn in looking at their abilities, performance, and ease of use in today's development environments.

Results show that GitHub Copilot is more efficient and quicker than other software, particularly for Python. Even so, JavaScript compatibility problems in most of the software persist. Results also show that great documentation, community involvement, and simplicity of interfaces are required in AI software. Developers are highly effective but are concerned with prices, poor features, and integration issues.The research concludes that AI-assisted software tools are revolutionary to software development and have huge gains in efficiency. Their usage requires, however, vigilant care of accuracy, security, and ethics. Suggestions given are further flexibility of the tools, more mainstream language support, e.g., with Python, and extending an invitation to open-source communities to collaborate with developers. The research contributes to the body of knowledge of AI adoption for software development, with practical ideas for developers, organizations, and tool builders.