14 citations · 42 across the 9 of their papers we have counts for
5 papers · 1 filter
What Is Wrong with My Model? Identifying Systematic Problems with Semantic Data Slicing
Chenyang Yang, Yining Hong, Grace A. Lewis +2
Machine learning models make mistakes, yet sometimes it is difficult to identify the systematic problems behind the mistakes. Practitioners engage in various activities, including…
(Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIs
Wanqin Ma, Chenyang Yang, Christian Kästner
Large Language Models (LLMs) are increasingly integrated into software applications. Downstream application developers often access LLMs through APIs provided as a service. However…
Accelerating Patch Validation for Program Repair with Interception-Based Execution Scheduling
Yuan-An Xiao, Chenyang Yang, Bo Wang +1
Long patch validation time is a limiting factor for automated program repair (APR). Though the duality between patch validation and mutation testing is recognized, so far there exi…
A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges
Jenny T. Liang, Chenyang Yang, Brad A. Myers
The software engineering community recently has witnessed widespread deployment of AI programming assistants, such as GitHub Copilot. However, in practice, developers do not accept…
Data Leakage in Notebooks: Static Detection and Better Processes
Chenyang Yang, Rachel A Brower-Sinning, Grace A. Lewis +1
Data science pipelines to train and evaluate models with machine learning may contain bugs just like any other code. Leakage between training and test data can lead to overestimati…