works on

From the 3 of 8 linked papers with an AI index.

collaborators

8 papers

cs.SE2026

Structural Validation of LLM-Generated Microservice Decompositions Using Source-Code Dependencies

Daniel Silva, Renan Alves, Emanuel Dantas Filho +5

The paper assesses how well microservice decompositions generated by large language models match the actual source-code dependencies, using static analysis and metrics for dependen…

cs.SE2026

From Textual Requirements to Microservice Architectures - A Comprehensive Evaluation of LLM-Based Design Synthesis

Danyllo Albuquerque, José Renan, Guillermo Rodríguez +5

The paper evaluates whether a large language model (OpenAI o3) can automatically generate microservice architectures from textual requirements, comparing zero-shot and few-shot pro…

cs.SE2026

AI-Conducted Interviews in Empirical Software Engineering: An Experience Report

Rohit Gheyi, Danyllo Albuquerque, Márcio Ribeiro +1

The paper reports on using a customized AI system (MyGPT) to conduct short, self‑administered, voice‑based interviews in empirical software engineering studies, evaluating particip…

cs.SE2026

Prompting GPT-5 on Scrum Certification Questions: An Empirical Accuracy Study

Mirko Perkusich, Danyllo Albuquerque, João Paiva +6

Large Language Models (LLMs) are increasingly used in Agile Software Development for documentation, coaching, and training. As practitioners adopt these tools to prepare for certif…

cs.SE2026

Comparing Large Language Models on Scrum Certification-Style Questions: Accuracy, Stability, and Error Patterns

Robson Alves Vilar, Emanuel Dantas Filho, Ademar França de Sousa Neto +5

Large Language Models (LLMs) are increasingly used in exam- and certification-style question answering tasks, where their ability to retrieve, interpret, and apply domain-specific…

cs.LG2026

On the Sensitivity of Firing Rate-Based Federated Spiking Neural Networks to Differential Privacy

Luiz Pereira, Mirko Perkusich, Dalton Valadares +1

Federated Neuromorphic Learning (FNL) enables energy-efficient and privacy-preserving learning on devices without centralizing data. However, real-world deployments require additio…