AI-assisted programming tools, such as Github Copilot, often lead to lower task completion rates compared to traditional methods like Intellisense. The task completion time showed minimal improvement. Assessing correctness in code produced by these tools is challenging, resulting in inefficiencies when programmers encounter fundamental flaws, which can send them on fruitless debugging journeys. The difficulties in writing effective prompts and transitioning programming activities toward checking and debugging highlight the limits of existing metaphors for understanding AI-assisted programming.
Participants in a comparative study of Github Copilot vs Intellisense failed to complete tasks more with Copilot, showing no notable improvement in task completion time.
Assessing the correctness of code generated by AI tools remains difficult and can create an efficiency bottleneck, especially when fundamental flaws lead to unraveling debugging efforts.
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