AI-Driven Code Generation:Challenges and Opportunities

The recent emergence of large language models (LLMs), followed by AI-assisted coding assistants, revolutionized the traditional software development industry by introducing AI-assisted coding tools like GitHub Copilot, Cursor, Windsurf, and Replit. These tools enhance productivity, reduce repetitive tasks by assisting developers, and support developing and maintaining comprehensive applications. Despite their growing popularity and adoption, their effectiveness, limitations, weaknesses, and real-world impact remain underexplored, particularly in complex development scenarios. This research investigates the capabilities, challenges, and opportunities of AI Coding Assistants throughout the development life cycle by developing a comprehensive web application with the aid of the said tools as a case study from the perspective of a solo developer working without the support of a dedicated development team. The study evaluates key dimensions such as AI assistant task coverage capabilities, prompt intent understanding and robustness, debugging, testing capabilities, and documentation. A comparative analysis of tools like Replit, Cursor, Windsurf, and GitHub Copilot highlights their strengths and weaknesses. By demonstrating the practical use cases of AI Coding Assistants, this study provides insights into their potential to enhance software development and support solo developers while emphasizing the need for careful supervision.