This article examines the impact and effectiveness of AI code generation tools, particularly GitHub Copilot. It utilizes a mixed-method approach to gather data on user experiences, revealing both the benefits—such as improved coding speed—and challenges like errors in code generation and user misinterpretation of suggestions. The researchers provide insights into the types of problems users face, the causes behind these issues, and potential solutions. Implications include necessary adaptations for both users and developers to optimize the use of AI tools in software development.
This study reveals that while GitHub Copilot has significantly improved the coding speed of users, it has also introduced unique challenges that need addressing.
Through a mixed-method approach, we collected qualitative and quantitative data to analyze user interactions with Copilot, identifying various problems, causes, and potential solutions.
The implications of our findings suggest that both Copilot users and developers must adapt their practices to maximize the benefits of AI while mitigating associated risks.
Future research should explore the long-term effects of AI-assisted coding on software development culture, best practices, and the evolving role of developers.
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