Enhancing Software Development with AI : A Case Study on Generative AI's Impact on Full-Stack Development

Sammanfattning

This study evaluates generative AI’s effectiveness in generating full-stack application code based on detailed requirements. The research question is: “How effectively can generative AI, specifically ChatGPT-4, generate full-stack application code that aligns with detailed requirements specifications?” Despite AI's potential, research in this area is limited. This study explores its strengths and limitations, contributing to both academia and industry. The methodology consists of two main phases. In the first, a case study was conducted to test different prompting techniques in order to identify the most effective one. Four participants with experience in full-stack development and generative AI evaluated the techniques by creating a simple application and assessing them based on ease of use, accuracy, functionality, and code quality. The goal was to select the most effective technique for use in the second phase. The techniques tested included Zero-shot learning, One-shot learning, Few-shot learning, Prompt Chaining, and Multimodal Prompting. Of these, Prompt Chaining proved to be the most effective, increasing detail and accuracy through a two-step process. The second phase, which is the main phase of the study, involved the creation of five different applications in full-stack development. These applications were designed to test AI's ability to generate functional and correct code in more complex development scenarios. They were evaluated based on the criterias: accuracy, responsiveness, functionality and code quality. The results show that AI has the potential to generate code that meets many of the stated requirements, but that there are still limitations. AI showed strong performance in generating fullstack code, accurately capturing requirements and functional elements. However, challenges arose with complex layouts, responsive design, and connecting front-end to back-end. While code quality was generally qualitative, issues with maintainability and CSS practices were noted. This study highlights AI's potential to accelerate development but underscores the need for precise input and additional refinement.