6 citations · 12 across the 11 of their papers we have counts for
13 papers
Adaptive Strategy Generation for Boundary Value Exploration Beyond Numeric Inputs
Sabinakhon Akbarova, Felix Dobslaw, Robert Feldt
Software behavior often changes abruptly at boundaries between input regions, and these transitions are known to be fault-prone. Boundary Value Exploration (BVE) automates boundary…
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…
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…
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…
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…
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…
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…
Qualitative Research Methods in Software Engineering: Past, Present, and Future
Carolyn Seaman, Rashina Hoda, Robert Feldt
The paper entitled "Qualitative Methods in Empirical Studies of Software Engineering" by Carolyn Seaman was published in TSE in 1999. It has been chosen as one of the most influent…
The Social Psychology of Software Security (Psycurity)
Lucas Gren, Robert Feldt
This position paper explores the intricate relationship between social psychology and secure software engineering, underscoring the vital role social psychology plays in the realm…