Measuring GitHub Copilot's Impact on Productivity

被引:2
|
作者
Ziegler, Albert [1 ]
Kalliamvakou, Eirini [1 ]
Li, X. Alice [1 ]
Rice, Andrew [1 ]
Rifkin, Devon [1 ]
Simister, Shawn [1 ]
Sittampalam, Ganesh [1 ]
Aftandilian, Edward [1 ]
机构
[1] GitHub Inc, San Francisco, CA 94107 USA
关键词
D O I
10.1145/3633453
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Case study asks Copilot users about its impact on their productivity, and seeks to find their perceptions mirrored in user data. CODE-COMPLETION SYSTEMS OFFERING suggestions to a developer in their integrated development environment (IDE) have become the most frequently used kind of programmer assistance.(1) When generating whole snippets of code, they typically use a large language model (LLM) to predict what the user might type next (the completion) from the context of what they are working on at the moment (the prompt).(2) This system allows for completions at any position in the code, often spanning multiple lines at once.
引用
收藏
页码:54 / 63
页数:10
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