2 citations · 2 across the 4 of their papers we have counts for
4 papers
Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs
Juyeon Yoon, Somin Kim, Robert Feldt +1
Software increasingly relies on the emergent capabilities of Large Language Models (LLMs), from natural language understanding to program analysis and generation. Yet testing them…
Capturing Semantic Flow of ML-based Systems
Shin Yoo, Robert Feldt, Somin Kim +1
ML-based systems are software systems that incorporates machine learning components such as Deep Neural Networks (DNNs) or Large Language Models (LLMs). While such systems enable a…
DANDI: Diffusion as Normative Distribution for Deep Neural Network Input
Somin Kim, Shin Yoo
Surprise Adequacy (SA) has been widely studied as a test adequacy metric that can effectively guide software engineers towards inputs that are more likely to reveal unexpected beha…
Benchmarking Foundation Models on Exceptional Cases: Dataset Creation and Validation
Suho Kang, Jungyang Park, Joonseo Ha +4
Foundation models (FMs) have achieved significant success across various tasks, leading to research on benchmarks for reasoning abilities. However, there is a lack of studies on FM…