5.1k citations · 9.6k across the 102 of their papers we have counts for
7 papers · 1 filter
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…
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu +6
As LLMs are increasingly used as judges in code applications, they should be evaluated in realistic interactive settings that capture partial context and ambiguous intent. We prese…
Beyond the Commit: Developer Perspectives on Productivity with AI Coding Assistants
Valerie Chen, Jasmyn He, Behnjamin Williams +2
Measuring developer productivity is a topic that has attracted attention from both academic research and industrial practice. In the age of AI coding assistants, it has become even…
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…
Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows
Valerie Chen, Ameet Talwalkar, Robert Brennan +1
Developers now have access to a growing array of increasingly autonomous AI tools for software development. While many studies examine copilots that provide chat assistance or code…
Copilot Arena: A Platform for Code LLM Evaluation in the Wild
Wayne Chi, Valerie Chen, Anastasios Nikolas Angelopoulos +7
Evaluating in-the-wild coding capabilities of large language models (LLMs) is a challenging endeavor with no clear solution. We introduce Copilot Arena, a platform to collect user…
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers
Hussein Mozannar, Valerie Chen, Mohammed Alsobay +7
Evaluation of large language models for code has primarily relied on static benchmarks, including HumanEval (Chen et al., 2021), or more recently using human preferences of LLM res…