2 citations · 8 across the 13 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…
Towards AI Agents Supported Research Problem Formulation
Anrafel Fernandes Pereira, Maria Teresa Baldassarre, Daniel Mendez +1
Poorly formulated research problems can compromise the practical relevance of Software Engineering studies by not reflecting the complexities of industrial practice. This vision pa…
Attributes to Support the Formulation of Practically Relevant Research Problems in Software Engineering
Anrafel Fernandes Pereira, Maria Teresa Baldassarre, Daniel Mendez +9
[Background] A well-formulated research problem is essential for achieving practical relevance in Software Engineering (SE), yet there is a lack of structured guidance in this earl…
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…
Towards Lean Research Inception: Assessing Practical Relevance of Formulated Research Problems
Anrafel Fernandes Pereira, Marcos Kalinowski, Maria Teresa Baldassarre +3
[Context] The lack of practical relevance in many Software Engineering (SE) research contributions is often rooted in oversimplified views of industrial practice, weak industry con…
Who "Controls" Where Work Shall be Done? State-of-Practice in Post-Pandemic Remote Work Regulation
Darja Smite, Nils Brede Moe, Maria Teresa Baldassarre +8
The COVID-19 pandemic has permanently altered workplace structures, making remote work a widespread practice. While many employees advocate for flexibility, many employers reconsid…
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…
Teaching Survey Research in Software Engineering
Marcos Kalinowski, Allysson Allex Araújo, Daniel Mendez
In this chapter, we provide advice on how to effectively teach survey research based on lessons learned from several international teaching experiences on the topic and from conduc…
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…
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…