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Researcher profile

Henry Muccini

Henry Muccini contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Software EngineeringArtificial Intelligencecs.CYHuman-Computer InteractionMultiagent SystemsRobotics

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Published work

3 published item(s)

preprint2026arXiv

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

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 systems themselves are complex systems that require re-thinking currently established software engineering practices. To chart a coherent research agenda covering the two directions, we organized the A2SE seminar in Rio de Janeiro, bringing together 18 experts from academia and industry. Through structured presentations, collaborative topic clustering, and focused group discussions, participants identified six thematic areas: Governance, Software Engineering for Agents, Agents for Software Architecture, Quality and Evaluation, Sustainability, and Code, and they prioritized short-term and long-term research directions for each. This paper presents the resulting community-driven, opinionated research agenda, offering the SE community a structured foundation for coordinating efforts at this critical juncture.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Designing Internet of Behaviors Systems

The Internet of Behaviors (IoB) puts human behavior at the core of engineering intelligent connected systems. IoB links the digital world to human behavior to establish human-driven design, development, and adaptation processes. This paper defines the novel concept by an IoB model based on a collective effort interacting with software engineers, human-computer interaction scientists, social scientists, and cognitive science communities. The model for IoB is created based on an exploratory study that synthesizes state-of-the-art analysis and experts interviews. The architecture of a real industry 4.0 manufacturing infrastructure helps to explain the IoB model and it's application. The conceptual model was used to successfully implement a socio-technical infrastructure for a crowd monitoring and queue management system for the Uffizi Galleries, Florence, Italy. The experiment, which started in the fall of 2016 and was operational in the fall of 2018, used a data-driven approach to feed the system with real-time sensory data. It also incorporated prediction models on visitors' mobility behavior. The system's main objective was to capture human behavior, model it, and build a mechanism that considers changes, adapts in real-time, and continuously learns from repetitive behaviors. In addition to the conceptual model and the real-life evaluation, this paper provides recommendations from experts and gives future directions for IoB to become a significant technological advancement in the coming few years.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review

Big data applications are currently used in many application domains, ranging from statistical applications to prediction systems and smart cities. However, the quality of these applications is far from perfect, leading to a large amount of issues and problems. Consequently, assuring the overall quality for big data applications plays an increasingly important role. This paper aims at summarizing and assessing existing quality assurance (QA) technologies addressing quality issues in big data applications. We have conducted a systematic literature review (SLR) by searching major scientific databases, resulting in 83 primary and relevant studies on QA technologies for big data applications. The SLR results reveal the following main findings: 1) the impact of the big data attributes of volume, velocity, and variety on the quality of big data applications; 2) the quality attributes that determine the quality for big data applications include correctness, performance, availability, scalability, reliability and so on; 3) the existing QA technologies, including analysis, specification, model-driven architecture (MDA), verification, fault tolerance, testing, monitoring and fault & failure prediction; 4) existing strengths and limitations of each kind of QA technology; 5) the existing empirical evidence of each QA technology. This study provides a solid foundation for research on QA technologies of big data applications. However, many challenges of big data applications regarding quality still remain.

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