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Developer Tools and Programming Support / LLM Code Generation and Programming Assistants

When programmers modify code with handwritten annotations, tools cannot automatically understand and transform their intent.

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

Similar questions

lightbulbPractical problemLLM Code Generation and Programming Assistants

Users often feel lost when setting up LLM workflows and evaluation tasks.

CHI '25ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines

lightbulbPractical problemLLM Code Generation and Programming Assistants

Non-technical users struggle to efficiently describe programming tasks in natural language to interact with AI.

CHI '25How Humans Communicate Programming Tasks in Natural Language and Implications For End-User Programming with LLMs

lightbulbPractical problemLLM Code Generation and Programming Assistants

Scientists may produce experimental analysis errors due to programming mistakes, undermining credibility of research conclusions.

CHI '25How Scientists Use Large Language Models to Program

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.

CHI '25Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software

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

lightbulbPractical problemLLM Code Generation and Programming Assistants

Novice programmers struggle to efficiently generate correct code with existing code generation tools.

CHI '24How Beginning Programmers and Code LLMs (Mis)read Each Other

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ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines

Jingyue Zhang, Ian Arawjo

How Humans Communicate Programming Tasks in Natural Language and Implications For End-User Programming with LLMs

Madison Pickering, Helena Williams, Alison Gan

How Scientists Use Large Language Models to Program

Gabrielle O'Brien

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

How Beginning Programmers and Code LLMs (Mis)read Each Other

Sydney Nguyen, Hannah McLean Babe, Yangtian Zi

"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

Exploring the Learnability of Program Synthesizers by Novice Programmers

Dhanya Jayagopal, Justin Lubin, Sarah E. Chasins