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Rodrigo Falcão

Rodrigo Falcão appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Software EngineeringArtificial IntelligenceMultiagent Systems

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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

Business Process Model for Interoperability Improvement in the Agricultural Domain Using Digital Twins

A farm generates a lot of data from various systems, which is then stored in a distributed manner, usually in non-standardized formats, which bears the risk of data inconsistencies. This work addresses this issue by using business process management (BPM) to demonstrate that the use of digital twins (DTs) can improve interoperability between services in the agriculture domain. Steps from the BPM lifecycle were applied to a farming use case in Germany. First, the as-is business process model was discovered and modeled without DTs, analyzed and then redesigned into the to-be model according to the DT integration. The to-be model showed a reduction in the number of tasks needed to be performed by the farmer as well as an improvement of process data quality, interoperability, and efficiency. Finally, a comparison of the' average processing times of both models with the help of process simulation revealed improvements in the to-be process.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

xPACE and TASC Modeler: Tool support for data-driven context modeling

From a requirements engineering point of view, the elicitation of context-aware functionalities calls for context modeling, an early step that aims at understanding the application contexts and how it may influence user tasks. In practice, however, context modeling activities have been overlooked by practitioners due to their high complexity. To improve this situation, we implemented xPACE and TASC Modeler, which are tools that support the automation of context modeling based on existing contextual data. In this demonstration paper, we present our implementation of a data-driven context modeling approach, which is composed of a contextual data processor (xPACE) and a context model generator (TASC Modeler). We successfully evaluated the results provided by the tools in a software development project.

0 citations0 reviewsNo code linkedOpen access