FedCSD: A Federated Learning Based Approach for Code-Smell Detection

arXiv:2306.00038v3 Announce Type: replace-cross
Abstract: This paper proposes a Federated Learning Code Smell Detection (FedCSD) approach that allows organizations to collaboratively train federated ML models while preserving their data privacy. These assertions have been supported by three experiments that have significantly leveraged three manually validated datasets aimed at detecting and examining different code smell scenarios. In experiment 1, which was concerned with a centralized training experiment, dataset two achieved the lowest accuracy (92.30%) with fewer smells, while datasets one and …

abstract arxiv code cs.ai cs.lg cs.se data data privacy datasets detection federated learning ml models organizations paper privacy train type