2 citations · 6 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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 such as ChatGPT particularly accessible to novice progra…
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…
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…
Industrial Practices of Requirements Engineering for ML-Enabled Systems in Brazil
Antonio Pedro Santos Alves, Marcos Kalinowski, Daniel Mendez +4
[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unreal…
Naming the Pain in Machine Learning-Enabled Systems Engineering
Marcos Kalinowski, Daniel Mendez, Görkem Giray +12
Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to…
ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems
Eduardo Zimelewicz, Marcos Kalinowski, Daniel Mendez +13
[Context] Systems incorporating Machine Learning (ML) models, often called ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are en…
Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey
Antonio Pedro Santos Alves, Marcos Kalinowski, Görkem Giray +12
Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (R…