Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 21 Mar 2024.
Builds on
8 of 17 references.
References · 8
- 2023Code Llama: Open Foundation Models for Codecited by 2,105
- 2022Selective Annotation Makes Language Models Better Few-Shot Learnerscited by 242
- 2023How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environmentcited by 55
- 2023Investigating and Designing for Trust in AI-powered Code Generation Toolscited by 34
- 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 28
- 2024Unsupervised Evaluation of Code LLMs with Round-Trip Correctnesscited by 23
- 2023The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generationcited by 20
- 2023Better Language Models of Code through Self-Improvementcited by 14
Built on by
Citing papers per year: 2025: 2.
2 papers.