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Federal University of Campina Grande - UFCG
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Few-shot prompting boosts LLM performance in generating microservice architectures, achieving an impressive F1 score of 0.97 for service identification.
Structural evaluations of LLM-generated microservices reveal that apparent differences in prompting strategies may mask underlying methodological biases rather than true architectural quality.
AI-driven interviews achieved a 90.9% positive experience rating, but raised concerns about depth and privacy that could redefine ESE methodologies.
Differential Privacy can wreak havoc on the delicate firing rate dynamics of federated spiking neural networks, leading to instability in client selection and attenuated aggregation.
