1 citations · 2 across the 5 of their papers we have counts for
6 papers
Learning from 53.6K Real-World Developer Edits of AI-Generated Code
Jenny T. Liang, Mihika Bairathi, Wayne Chi +3
Imperfections in AI-generated code require that software developers modify the generated code manually, or by re-prompting an AI programming assistant. Manual code edits provide mo…
Mapping the Design Space of User Experience for Computer Use Agents
Ruijia Cheng, Jenny T. Liang, Eldon Schoop +1
Large language model (LLM)-based computer use agents execute user commands by interacting with available UI elements, but little is known about how users want to interact with thes…
EDIT-Bench: Evaluating LLM Abilities to Perform Real-World Instructed Code Edits
Wayne Chi, Valerie Chen, Ryan Shar +8
Instructed code editing, where LLMs directly modify a developer's existing code based on a user instruction, is becoming a widely used interaction mode in AI coding assistants. How…
Understanding User Experiences of Computer Use Agents: Design Space and Opportunities for Building Agent UX Prototypes
Jenny T. Liang, Titus Barik, Jeffrey Nichols +2
Computer use agents (or "agents") are generative AI that automates actions within user interfaces from user commands. Current research focuses on training and evaluating the underl…
TableTalk: Scaffolding Spreadsheet Development with a Language Agent
Jenny T. Liang, Aayush Kumar, Yasharth Bajpai +7
Spreadsheet programming is challenging. Programmers use spreadsheet programming knowledge (e.g., formulas) and problem-solving skills to combine actions into complex tasks. Advance…
How Developers Choose Debugging Strategies for Challenging Web Application Defects
Maryam Arab, Jenny T. Liang, Valentina Hong +1
Effective debugging is a crucial aspect of software development, demanding problem-solving skills, expertise, and appropriate tools. Although previous research has studied expert d…