# GitHub Copilot: the perfect Code compLeeter?

arXiv: https://arxiv.org/abs/2406.11326 · HTML: https://mlsanity.com/paper/2406.11326/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [Piloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?](https://mlsanity.com/paper/2210.14699/) (2022)
- [To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?](https://mlsanity.com/paper/2204.06894/) (2022)

## Built on by (most cited first, 4 of 4)

- [Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization](https://mlsanity.com/paper/2503.02312/) (2025)
- [Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmers](https://mlsanity.com/paper/2411.09224/) (2024)
- [Scaling Small Agents Through Strategy Auctions](https://mlsanity.com/paper/2602.02751/) (2026)
- [The Role of GitHub Copilot on Software Development: A Perspective on Productivity, Security, Best Practices and Future Directions](https://mlsanity.com/paper/2502.13199/) (2025)

## Abstract (arXiv)

This paper aims to evaluate GitHub Copilot's generated code quality based on the LeetCode problem set using a custom automated framework. We evaluate the results of Copilot for 4 programming languages: Java, C++, Python3 and Rust. We aim to evaluate Copilot's reliability in the code generation stage, the correctness of the generated code and its dependency on the programming language, problem's difficulty level and problem's topic. In addition to that, we evaluate code's time and memory efficiency and compare it to the average human results. In total, we generate solutions for 1760 problems for each programming language and evaluate all the Copilot's suggestions for each problem, resulting in over 50000 submissions to LeetCode spread over a 2-month period. We found that Copilot successfully solved most of the problems. However, Copilot was rather more successful in generating code in Java and C++ than in Python3 and Rust. Moreover, in case of Python3 Copilot proved to be rather unreliable in the code generation phase. We also discovered that Copilot's top-ranked suggestions are not always the best. In addition, we analysed how the topic of the problem impacts the correctness rate. Finally, based on statistics information from LeetCode, we can conclude that Copilot generates more efficient code than an average human.