lightbulbPractical problemLLM Code Generation and Programming Assistants
Non-professional programming users struggle to generate accurate code through natural language.Developer Tools and Programming Support / LLM Code Generation and Programming Assistants
Non-professional programming users struggle to generate accurate code through natural language.
Similar questions
lightbulbPractical problemLLM Code Generation and Programming Assistants
Users often feel lost when setting up LLM workflows and evaluation tasks.lightbulbPractical problemLLM Code Generation and Programming Assistants
Non-technical users struggle to efficiently describe programming tasks in natural language to interact with AI.lightbulbPractical problemLLM Code Generation and Programming Assistants
Scientists may produce experimental analysis errors due to programming mistakes, undermining credibility of research conclusions.lightbulbPractical problemLLM Code Generation and Programming Assistants
Users struggle to learn software features while using AI assistants to complete tasks and lack a sense of control.lightbulbPractical problemLLM Code Generation and Programming Assistants
When programmers modify code with handwritten annotations, tools cannot automatically understand and transform their intent.UIST '24Code Shaping: Iterative Code Editing with Free-form Sketching
lightbulbPractical problemLLM Code Generation and Programming Assistants
Traditional LLM interfaces may reduce user creativity and efficiency, affecting testing outcomes.UIST '24Pay Attention! Human-Centric Improvements of LLM-based Interfaces for Assisting Software Test Case Development
Related papers
CHI 2025
ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines
Jingyue Zhang, Ian Arawjo
CHI 2025
How Humans Communicate Programming Tasks in Natural Language and Implications For End-User Programming with LLMs
Madison Pickering, Helena Williams, Alison Gan
CHI 2025
How Scientists Use Large Language Models to Program
Gabrielle O'Brien
CHI 2025
Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software
Anjali Khurana, Xiaotian Su, April Yi Wang
CHI 2024
How Beginning Programmers and Code LLMs (Mis)read Each Other
Sydney Nguyen, Hannah McLean Babe, Yangtian Zi
CHI 2023
"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
Michael Xieyang Liu, Advait Sarkar, Carina Negreanu
UIST 2022
Exploring the Learnability of Program Synthesizers by Novice Programmers
Dhanya Jayagopal, Justin Lubin, Sarah E. Chasins