Copilot for Xcode: Exploring AI-Assisted Programming by Prompting
Cloud-based Large Language Models
- URL: http://arxiv.org/abs/2307.14349v1
- Date: Sat, 8 Jul 2023 09:11:19 GMT
- Title: Copilot for Xcode: Exploring AI-Assisted Programming by Prompting
Cloud-based Large Language Models
- Authors: Chee Wei Tan, Shangxin Guo, Man Fai Wong, Ching Nam Hang
- Abstract summary: Copilot for Xcode is an AI-assisted programming tool for program composition and design to support human software developers.
By seamlessly integrating cloud-based Large Language Models (LLM) with Apple's local development environment, Xcode, this tool enhances productivity and unleashes creativity for software development in Apple software ecosystem.
- Score: 2.5272389610447856
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents an AI-assisted programming tool called Copilot for Xcode
for program composition and design to support human software developers. By
seamlessly integrating cloud-based Large Language Models (LLM) with Apple's
local development environment, Xcode, this tool enhances productivity and
unleashes creativity for software development in Apple software ecosystem
(e.g., iOS apps, macOS). Leveraging advanced natural language processing (NLP)
techniques, Copilot for Xcode effectively processes source code tokens and
patterns within code repositories, enabling features such as code generation,
autocompletion, documentation, and error detection. Software developers can
also query and make "small" decisions for program composition, some of which
can be made simultaneously, and this is facilitated through prompt engineering
in a chat interface of Copilot for Xcode. Finally, we present simple case
studies as evidence of the effectiveness of utilizing NLP in Xcode to prompt
popular LLM services like OpenAI ChatGPT for program composition and design.
Related papers
- Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing [0.9668407688201359]
generative artificial intelligence (GenAI) is poised to transform productivity in scientific computing.
We developed a tool, CodeScribe, which combines prompt engineering with user supervision to establish an efficient process for code conversion.
We also address the challenges of AI-driven code translation and highlight its benefits for enhancing productivity in scientific computing.
arXiv Detail & Related papers (2024-10-31T16:48:41Z) - Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework [8.28588489551341]
This paper presents CAMP, a multi-model AI-assisted programming framework that consists of a local model that employs Retrieval-Augmented Generation (RAG)
RAG retrieves contextual information from the cloud model to facilitate context-aware prompt construction.
The methodology is actualized in Copilot for Xcode, an AI-assisted programming tool crafted for the Apple software ecosystem.
arXiv Detail & Related papers (2024-10-20T04:51:24Z) - OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer.
We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
arXiv Detail & Related papers (2024-07-23T17:50:43Z) - Cross Language Soccer Framework: An Open Source Framework for the RoboCup 2D Soccer Simulation [0.4660328753262075]
RoboCup Soccer Simulation 2D (SS2D) research is hampered by the complexity of existing Cpp-based codes like Helios, Cyrus, and Gliders.
This development paper introduces a transformative solution a g-based, language-agnostic framework that seamlessly integrates with the high-performance Helios base code.
arXiv Detail & Related papers (2024-06-09T03:11:40Z) - Automatic Programming: Large Language Models and Beyond [48.34544922560503]
We study concerns around code quality, security and related issues of programmer responsibility.
We discuss how advances in software engineering can enable automatic programming.
We conclude with a forward looking view, focusing on the programming environment of the near future.
arXiv Detail & Related papers (2024-05-03T16:19:24Z) - CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models.
CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
arXiv Detail & Related papers (2024-04-03T02:21:46Z) - ChatDev: Communicative Agents for Software Development [84.90400377131962]
ChatDev is a chat-powered software development framework in which specialized agents are guided in what to communicate.
These agents actively contribute to the design, coding, and testing phases through unified language-based communication.
arXiv Detail & Related papers (2023-07-16T02:11:34Z) - Natural Language Generation and Understanding of Big Code for
AI-Assisted Programming: A Review [9.355153561673855]
This paper focuses on transformer-based large language models (LLMs) trained using Big Code.
LLMs have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection.
It explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications.
arXiv Detail & Related papers (2023-07-04T21:26:51Z) - SheetCopilot: Bringing Software Productivity to the Next Level through
Large Language Models [60.171444066848856]
We propose a SheetCopilot agent that takes natural language task and control spreadsheet to fulfill the requirements.
We curate a representative dataset containing 221 spreadsheet control tasks and establish a fully automated evaluation pipeline.
Our SheetCopilot correctly completes 44.3% of tasks for a single generation, outperforming the strong code generation baseline by a wide margin.
arXiv Detail & Related papers (2023-05-30T17:59:30Z) - From Copilot to Pilot: Towards AI Supported Software Development [3.0585424861188844]
We study the limitations of AI-supported code completion tools like Copilot and offer a taxonomy for understanding the classification of AI-supported code completion tools in this space.
We then conduct additional investigation to determine the current boundaries of AI-supported code completion tools like Copilot.
We conclude by providing a discussion on challenges for future development of AI-supported code completion tools to reach the design level of abstraction in our taxonomy.
arXiv Detail & Related papers (2023-03-07T18:56:52Z) - AVATAR: A Parallel Corpus for Java-Python Program Translation [77.86173793901139]
Program translation refers to migrating source code from one language to another.
We present AVATAR, a collection of 9,515 programming problems and their solutions written in two popular languages, Java and Python.
arXiv Detail & Related papers (2021-08-26T05:44:20Z)
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.