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Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals

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arxiv 2403.14592 v1 pith:HFJ6SDM5 submitted 2024-03-21 cs.SE cs.AIcs.HC

Khanh Nghiem , Anh Minh Nguyen , Nghi D. Q. Bui This is my paper · ORCID
classification cs.SEcs.AIcs.HC
keywords assistantscodingnext-generationshouldacademiaaddressadvancedai4se
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As a research-product hybrid group in AI for Software Engineering (AI4SE), we present four key takeaways from our experience developing in-IDE AI coding assistants. AI coding assistants should set clear expectations for usage, integrate with advanced IDE capabilities and existing extensions, use extendable backend designs, and collect app data responsibly for downstream analyses. We propose open questions and challenges that academia and industry should address to realize the vision of next-generation AI coding assistants.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A survey of 80+ Deep Research systems that proposes a four-layer taxonomy (foundation models, tool use, planning, synthesis) and compares commercial and open-source implementations.

  2. Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency

    cs.SE 2025-04 conditional novelty 3.0 of 10

    Refactoring smelly Java code and adding context, role, or token-limit prompts cut LLM chain-of-thought token use by roughly 15-50% in this study, but the 'no quality loss' claim rests only on shallow similarity metrics.

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