44 citations · 104 across the 47 of their papers we have counts for
12 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…
Is Agent Code Less Maintainable Than Human Code?
Shaswat Patel, Betty Li Hou, Arun Purohit +4
Maintainability is a core dimension of software engineering, shaping how code is written, reviewed, and developed over time. While coding agents have demonstrated strong performanc…
RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions
Keyu He, Qianou Ma, Valerie Chen +2
Understanding how developers interact with AI coding assistants requires more than chat logs or git histories in isolation; it requires reconstructing the full context: which promp…
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…
SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories
Aditya Bharat Soni, Rajat Ghosh, Vaishnavi Bhargava +2
Software testing is crucial for ensuring the correctness and reliability of software systems. Automated generation of issue reproduction tests from natural language issue descripti…
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…
The OpenHands Software Agent SDK: A Composable and Extensible Foundation for Production Agents
Xingyao Wang, Simon Rosenberg, Juan Michelini +8
Agents are now used widely in the process of software development, but building production-ready software engineering agents is a complex task. Deploying software agents effectivel…
TOM-SWE: User Mental Modeling For Software Engineering Agents
Xuhui Zhou, Valerie Chen, Zora Zhiruo Wang +3
Recent advances in coding agents have made them capable of planning, editing, running, and testing complex code bases. Despite their growing ability in coding tasks, these systems…
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…