This study investigates the impact of GitHub Copilot on developer productivity, focusing on how users perceive their productivity and how this aligns with usage data. The research analyzes 2,631 survey responses from developers using GitHub Copilot and matches these with usage data collected from the IDE. The study finds that the acceptance rate of suggestions is a better predictor of perceived productivity than other measures. It also shows that acceptance rate varies significantly among developers and over time. The study supports the idea that acceptance rate can be used for coarse-grained monitoring of the performance of a neural code synthesis system. However, other approaches are needed for fine-grained investigation due to the many human factors involved. The study also finds that junior developers see the largest gains in productivity from using GitHub Copilot. The study highlights the importance of considering multiple dimensions of productivity, including satisfaction, performance, activity, communication, and efficiency. The study concludes that developer productivity is a multidimensional concept that cannot be summarized by a single metric. The study also finds that experienced developers are less likely to benefit from GitHub Copilot in terms of writing better code, but it can still assist their productivity in other ways. The study also finds that acceptance rate is positively correlated with aggregate productivity, but there is still unexplained variance. The study also finds that acceptance rate varies over time and is influenced by factors such as time of day and weekdays. The study concludes that GitHub Copilot has a significant impact on developer productivity, particularly for junior developers. The study also finds that the acceptance rate of suggestions is a better predictor of perceived productivity than other measures. The study also finds that the acceptance rate varies significantly among developers and over time. The study highlights the importance of considering multiple dimensions of productivity, including satisfaction, performance, activity, communication, and efficiency. The study concludes that developer productivity is a multidimensional concept that cannot be summarized by a single metric. The study also finds that experienced developers are less likely to benefit from GitHub Copilot in terms of writing better code, but it can still assist their productivity in other ways. The study also finds that acceptance rate is positively correlated with aggregate productivity, but there is still unexplained variance. The study also finds that acceptance rate varies over time and is influenced by factors such as time of day and weekdays. The study concludes that GitHub Copilot has a significant impact on developer productivity, particularly for junior developers.This study investigates the impact of GitHub Copilot on developer productivity, focusing on how users perceive their productivity and how this aligns with usage data. The research analyzes 2,631 survey responses from developers using GitHub Copilot and matches these with usage data collected from the IDE. The study finds that the acceptance rate of suggestions is a better predictor of perceived productivity than other measures. It also shows that acceptance rate varies significantly among developers and over time. The study supports the idea that acceptance rate can be used for coarse-grained monitoring of the performance of a neural code synthesis system. However, other approaches are needed for fine-grained investigation due to the many human factors involved. The study also finds that junior developers see the largest gains in productivity from using GitHub Copilot. The study highlights the importance of considering multiple dimensions of productivity, including satisfaction, performance, activity, communication, and efficiency. The study concludes that developer productivity is a multidimensional concept that cannot be summarized by a single metric. The study also finds that experienced developers are less likely to benefit from GitHub Copilot in terms of writing better code, but it can still assist their productivity in other ways. The study also finds that acceptance rate is positively correlated with aggregate productivity, but there is still unexplained variance. The study also finds that acceptance rate varies over time and is influenced by factors such as time of day and weekdays. The study concludes that GitHub Copilot has a significant impact on developer productivity, particularly for junior developers. The study also finds that the acceptance rate of suggestions is a better predictor of perceived productivity than other measures. The study also finds that the acceptance rate varies significantly among developers and over time. The study highlights the importance of considering multiple dimensions of productivity, including satisfaction, performance, activity, communication, and efficiency. The study concludes that developer productivity is a multidimensional concept that cannot be summarized by a single metric. The study also finds that experienced developers are less likely to benefit from GitHub Copilot in terms of writing better code, but it can still assist their productivity in other ways. The study also finds that acceptance rate is positively correlated with aggregate productivity, but there is still unexplained variance. The study also finds that acceptance rate varies over time and is influenced by factors such as time of day and weekdays. The study concludes that GitHub Copilot has a significant impact on developer productivity, particularly for junior developers.