(untitled) (Research, Society and Development, 2022)
Non-functional requirements (NFRs) play an important role in the Software Engineering (SE) area, being associated with the construction, operation, and maintenance of a quality application. The success of the RNF classification manual task depends on the knowledge and experience of the requirements engineer and is time-consuming. Works have been developed aiming at the application of machine learning algorithms to automatically classify RNFs, a scenario in which the hyperparameters of the classifier model must be chosen. In this work, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to find hyperparameters of the Multilayer Neural Perceptron Network (MLP), with the objective of classifying NFRs present in the PROMISE_exp dataset. The GA found a combination of hyperparameters that gave an F1 of 0.6349, while the PSO found a combination that got 0.6426 of F1.