Demystifying Practices, Challenges and Expected Features of Using GitHub
Copilot
- URL: http://arxiv.org/abs/2309.05687v1
- Date: Mon, 11 Sep 2023 16:39:37 GMT
- Title: Demystifying Practices, Challenges and Expected Features of Using GitHub
Copilot
- Authors: Beiqi Zhang, Peng Liang, Xiyu Zhou, Aakash Ahmad, Muhammad Waseem
- Abstract summary: We conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub Discussions.
We identified the programming languages, technologies used with Copilot, functions implemented, benefits, limitations, and challenges when using Copilot.
Our results suggest that using Copilot is like a double-edged sword, which requires developers to carefully consider various aspects when deciding whether or not to use it.
- Score: 3.655281304961642
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the advances in machine learning, there is a growing interest in
AI-enabled tools for autocompleting source code. GitHub Copilot has been
trained on billions of lines of open source GitHub code, and is one of such
tools that has been increasingly used since its launch in June 2021. However,
little effort has been devoted to understanding the practices, challenges, and
expected features of using Copilot in programming for auto-completed source
code from the point of view of practitioners. To this end, we conducted an
empirical study by collecting and analyzing the data from Stack Overflow (SO)
and GitHub Discussions. We searched and manually collected 303 SO posts and 927
GitHub discussions related to the usage of Copilot. We identified the
programming languages, Integrated Development Environments (IDEs), technologies
used with Copilot, functions implemented, benefits, limitations, and challenges
when using Copilot. The results show that when practitioners use Copilot: (1)
The major programming languages used with Copilot are JavaScript and Python,
(2) the main IDE used with Copilot is Visual Studio Code, (3) the most common
used technology with Copilot is Node.js, (4) the leading function implemented
by Copilot is data processing, (5) the main purpose of users using Copilot is
to help generate code, (6) the significant benefit of using Copilot is useful
code generation, (7) the main limitation encountered by practitioners when
using Copilot is difficulty of integration, and (8) the most common expected
feature is that Copilot can be integrated with more IDEs. Our results suggest
that using Copilot is like a double-edged sword, which requires developers to
carefully consider various aspects when deciding whether or not to use it. Our
study provides empirically grounded foundations that could inform developers
and practitioners, as well as provide a basis for future investigations.
Related papers
- GitHub Copilot: the perfect Code compLeeter? [3.708656266586145]
This paper aims to evaluate GitHub Copilot's generated code quality based on the LeetCode problem set.
We evaluate Copilot's reliability in the code generation stage, the correctness of the generated code and its dependency on the programming language.
arXiv Detail & Related papers (2024-06-17T08:38:29Z) - OS-Copilot: Towards Generalist Computer Agents with Self-Improvement [48.29860831901484]
We introduce OS-Copilot, a framework to build generalist agents capable of interfacing with comprehensive elements in an operating system (OS)
We use OS-Copilot to create FRIDAY, a self-improving embodied agent for automating general computer tasks.
On GAIA, a general AI assistants benchmark, FRIDAY outperforms previous methods by 35%, showcasing strong generalization to unseen applications via accumulated skills from previous tasks.
arXiv Detail & Related papers (2024-02-12T07:29:22Z) - Exploring the Effect of Multiple Natural Languages on Code Suggestion
Using GitHub Copilot [46.822148186169144]
GitHub Copilot is an AI-enabled tool that automates program synthesis.
Recent studies have extensively examined Copilot's capabilities in various programming tasks.
However, little is known about the effect of different natural languages on code suggestion.
arXiv Detail & Related papers (2024-02-02T14:30:02Z) - Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study on GitHub and Stack Overflow [6.724815667295355]
GitHub Copilot, the AI programmer pair, utilize machine learning models trained on a large corpus of code snippets to generate code suggestions.
Despite its popularity in software development, there is limited empirical evidence on the actual experiences of practitioners who work with Copilot.
We collected data from 473 GitHub issues, 706 GitHub discussions, and 142 Stack Overflow posts.
arXiv Detail & Related papers (2023-11-02T06:24:38Z) - Collaborative, Code-Proximal Dynamic Software Visualization within Code
Editors [55.57032418885258]
This paper introduces the design and proof-of-concept implementation for a software visualization approach that can be embedded into code editors.
Our contribution differs from related work in that we use dynamic analysis of a software system's runtime behavior.
Our visualization approach enhances common remote pair programming tools and is collaboratively usable by employing shared code cities.
arXiv Detail & Related papers (2023-08-30T06:35:40Z) - Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow [49.724842920942024]
Industries such as finance, meteorology, and energy generate vast amounts of data daily.
We propose Data-Copilot, a data analysis agent that autonomously performs querying, processing, and visualization of massive data tailored to diverse human requests.
arXiv Detail & Related papers (2023-06-12T16:12:56Z) - Measuring the Runtime Performance of Code Produced with GitHub Copilot [1.6021036144262577]
We evaluate the runtime performance of code produced when developers use GitHub Copilot versus when they do not.
Our results suggest that using Copilot may produce code with a significantly slower runtime performance.
arXiv Detail & Related papers (2023-05-10T20:14:52Z) - Conversing with Copilot: Exploring Prompt Engineering for Solving CS1
Problems Using Natural Language [3.155277175705079]
GitHub Copilot is an artificial intelligence model for automatically generating source code from natural language problem descriptions.
Since June 2022, Copilot has officially been available for free to all students as a plug-in to development environments like Visual Studio Code.
arXiv Detail & Related papers (2022-10-27T03:48:24Z) - Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset [103.35624417260541]
Decentralized vehicle coordination is useful in understructured road environments.
We collect the Berkeley DeepDrive Drone dataset to study implicit "social etiquette" observed by nearby drivers.
The dataset is of primary interest for studying decentralized multiagent planning employed by human drivers and for computer vision in remote sensing settings.
arXiv Detail & Related papers (2022-09-19T05:06:57Z) - Level 2 Autonomous Driving on a Single Device: Diving into the Devils of
Openpilot [112.21008828205409]
Comma.ai claims one $999 aftermarket device mounted with a single camera and board inside owns the ability to handle L2 scenarios.
Together with open-sourced software of the entire system released by Comma.ai, the project is named Openpilot.
In this report, we would like to share our latest findings, shed some light on the new perspective of end-to-end autonomous driving from an industrial product-level side.
arXiv Detail & Related papers (2022-06-16T13:43:52Z) - An Empirical Cybersecurity Evaluation of GitHub Copilot's Code
Contributions [8.285068188878578]
GitHub Copilot is a language model trained over open-source GitHub code.
Code often contains bugs - and so, it is certain that the language model will have learned from exploitable, buggy code.
This raises concerns on the security of Copilot's code contributions.
arXiv Detail & Related papers (2021-08-20T17:30:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.