most citedQualitative Research Methods in Software Engineering: Past, Present, and Future

6 citations · 8 across the 7 of their papers we have counts for

collaborators
Showing cs.SEShow all

7 papers · 1 filter

cs.SE2026

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…

cs.SE20251 cited

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…

cs.SE2025

SETBVE: Quality-Diversity Driven Exploration of Software Boundary Behaviors

Sabinakhon Akbarova, Felix Dobslaw, Francisco Gomes de Oliveira Neto +1

Software systems exhibit distinct behaviors based on input characteristics, and failures often occur at the boundaries between input domains. Traditional Boundary Value Analysis (B…

cs.SE2025

Cross-Functional AI Task Forces (X-FAITs) for AI Transformation of Software Organizations

Lucas Gren, Robert Feldt

This experience report introduces the Cross-Functional AI Task Force (X-FAIT) framework to bridge the gap between strategic AI ambitions and operational execution within software-i…

cs.SE2025

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…

cs.SE2025

Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy

Felix Dobslaw, Robert Feldt, Juyeon Yoon +1

Large Language Models (LLMs) and Multi-Agent LLMs (MALLMs) introduce non-determinism unlike traditional or machine learning software, requiring new approaches to verifying correctn…