# Measuring the Runtime Performance of C++ Code Written by Humans using GitHub Copilot

arXiv: https://arxiv.org/abs/2305.06439 · HTML: https://mlsanity.com/paper/2305.06439/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2023.

## Built on by (most cited first, 5 of 5)

- [A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama](https://mlsanity.com/paper/2405.03616/) (2024)
- [A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI](https://mlsanity.com/paper/2510.26275/) (2025)
- [An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code](https://mlsanity.com/paper/2604.02352/) (2026)
- [Characterizing the Quality Profile of AI-Generated C++ in Production](https://mlsanity.com/paper/2608.06640/) (2026)
- [Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution](https://mlsanity.com/paper/2610.13563/) (2026)

## Abstract (arXiv)

GitHub Copilot is an artificially intelligent programming assistant used by many developers. While a few studies have evaluated the security risks of using Copilot, there has not been any study to show if it aids developers in producing code with better runtime performance. We evaluate the runtime performance of C++ code produced when developers use GitHub Copilot versus when they do not. To this end, we conducted a user study with 32 participants where each participant solved two C++ programming problems, one with Copilot and the other without it and measured the runtime performance of the participants' solutions on our test data. Our results suggest that using Copilot may produce C++ code with (statistically significant) slower runtime performance.