activity
20232026
most citedStatus Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey

2 citations · 6 across the 10 of their papers we have counts for

collaborators

10 papers

cs.SE2026

A Research Agenda on Agents and Software Engineering: Outcomes from the Rio A2SE Seminar

Davide Taibi, Henry Muccini, Karthik Vaidhyanathan +15

The rise of agentic AI is reshaping software engineering in two intertwined directions: agents are increasingly applied to support software engineering tasks, and Agentic AI system…

cs.SE2026

Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape

Bianca Trinkenreich, Fabio Calefato, Kelly Blincoe +8

Context: Software engineering (SE) researchers increasingly study Generative AI (GenAI) while also incorporating it into their own research practices. Despite rapid adoption, there…

cs.SE2026

Statistical Confidence in Functional Correctness: An Approach for AI Product Functional Correctness Evaluation

Wallace Albertini, Marina Condé Araújo, Júlia Condé Araújo +2

The quality assessment of Artificial Intelligence (AI) systems is a fundamental challenge due to their inherently probabilistic nature. Standards such as ISO/IEC 25059 provide a qu…

cs.HC2025

User Misconceptions of LLM-Based Conversational Programming Assistants

Gabrielle O'Brien, Antonio Pedro Santos Alves, Sebastian Baltes +3

Programming assistants powered by large language models (LLMs) have become widely available, with conversational assistants like ChatGPT particularly accessible to novice programme…

cs.SE2025

Define-ML: An Approach to Ideate Machine Learning-Enabled Systems

Silvio Alonso, Antonio Pedro Santos Alves, Lucas Romao +2

[Context] The increasing adoption of machine learning (ML) in software systems demands specialized ideation approaches that address ML-specific challenges, including data dependenc…

cs.SE2025

Investigating Issues that Lead to Code Technical Debt in Machine Learning Systems

Rodrigo Ximenes, Antonio Pedro Santos Alves, Tatiana Escovedo +2

[Context] Technical debt (TD) in machine learning (ML) systems, much like its counterpart in software engineering (SE), holds the potential to lead to future rework, posing risks t…