11 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…
Domain-Invariant Prompt Learning for Vision-Language Models
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt
Large pre-trained vision-language models like CLIP have transformed computer vision by aligning images and text in a shared feature space, enabling robust zero-shot transfer via pr…
DomAgent: Leveraging Knowledge Graphs and Case-Based Reasoning for Domain-Specific Code Generation
Shuai Wang, Dhasarathy Parthasarathy, Robert Feldt +1
Large language models (LLMs) have shown impressive capabilities in code generation. However, because most LLMs are trained on public domain corpora, directly applying them to real-…
Understanding on the Edge: LLM-generated Boundary Test Explanations
Sabinakhon Akbarova, Felix Dobslaw, Robert Feldt
Boundary value analysis and testing (BVT) is fundamental in software quality assurance because faults tend to cluster at input extremes, yet testers often struggle to understand an…
From Challenge to Change: Design Principles for AI Transformations
Theocharis Tavantzis, Stefano Lambiase, Daniel Russo +1
The rapid rise of Artificial Intelligence (AI) is reshaping Software Engineering (SE), creating new opportunities while introducing human-centered challenges. Although prior work n…
Large Language Models in Thematic Analysis: Prompt Engineering, Evaluation, and Guidelines for Qualitative Software Engineering Research
Cristina Martinez Montes, Robert Feldt, Cristina Miguel Martos +3
As artificial intelligence advances, large language models (LLMs) are entering qualitative research workflows, yet no reproducible methods exist for integrating them into establish…