7 citations · 19 across the 15 of their papers we have counts for
15 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…
An Empirical Study of Generative AI Adoption in Software Engineering
Görkem Giray, Onur Demirörs, Marcos Kalinowski +1
Context. GenAI tools are being increasingly adopted by practitioners in SE, promising support for several SE activities. Despite increasing adoption, we still lack empirical eviden…
Towards a Framework for Operationalizing the Specification of Trustworthy AI Requirements
Hugo Villamizar, Daniel Mendez, Marcos Kalinowski
Growing concerns around the trustworthiness of AI-enabled systems highlight the role of requirements engineering (RE) in addressing emergent, context-dependent properties that are…
Designing a Syllabus for a Course on Empirical Software Engineering
Paris Avgeriou, Nauman bin Ali, Marcos Kalinowski +1
Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. Wh…
Teaching Empirical Research Methods in Software Engineering: An Editorial Introduction
Daniel Mendez, Paris Avgeriou, Marcos Kalinowski +1
Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive…
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…
Professional Insights into Benefits and Limitations of Implementing MLOps Principles
Gabriel Araujo, Marcos Kalinowski, Markus Endler +1
Context: Machine Learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applicatio…
Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding
Raphael Cabral, Marcos Kalinowski, Maria Teresa Baldassarre +3
[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly h…
On the Interaction between Software Engineers and Data Scientists when building Machine Learning-Enabled Systems
Gabriel Busquim, Hugo Villamizar, Maria Julia Lima +1
In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges fro…
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…
A Multivocal Literature Review on the Benefits and Limitations of Automated Machine Learning Tools
Kelly Azevedo, Luigi Quaranta, Fabio Calefato +1
Context. Advancements in Machine Learning (ML) are revolutionizing every application domain, driving unprecedented transformations and fostering innovation. However, despite these…
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…
Identifying Concerns When Specifying Machine Learning-Enabled Systems: A Perspective-Based Approach
Hugo Villamizar, Marcos Kalinowski, Helio Lopes +1
Engineering successful machine learning (ML)-enabled systems poses various challenges from both a theoretical and a practical side. Among those challenges are how to effectively ad…