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Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models

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arxiv 2307.14349 v1 pith:HKNSKHJR submitted 2023-07-08 cs.SE cs.AI

Chee Wei Tan , Shangxin Guo , Man Fai Wong , Ching Nam Hang This is my paper · ORCID
classification cs.SEcs.AI
keywords xcodecopilotsoftwarecodecompositionlanguageprogramai-assisted
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

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  1. SimStep: Chain-of-Abstractions for Incremental Specification and Debugging of AI-Generated Interactive Simulations

    cs.HC 2025-07 conditional novelty 6.0 of 10

    SimStep guides teachers through four editable graph abstractions (concepts, scenario, learning goals, UI interactions) to generate and debug AI-built simulations without writing code.

  2. Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign

    cs.CR 2025-02 reject novelty 6.0 of 10

    RoSeMary embeds recoverable signatures in LLM-generated code and uses zero-knowledge proofs to verify ownership without revealing the signature.

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