Machine learning aplicado al análisis del rendimiento de desarrollos de software

Victor Daniel Gil-Vera; Cristian Seguro-Gallego

doi:10.33571/rpolitec.v18n35a9

Autores

Victor Daniel Gil-Vera Ingeniero de Sistemas, Docencia e Investigación, Grupo de Investigación SISCO. Universidad Católica Luis Amigó https://orcid.org/0000-0003-3895-4822
Cristian Seguro-Gallego Ingeniero de Sistemas, Docencia e Investigación, Grupo de Investigación SISCO. Universidad Católica Luis Amigó

DOI:

https://doi.org/10.33571/rpolitec.v18n35a9

Palavras-chave:

Análise de Desempenho, Inteligência Artificial, Modelação Preditiva, Testes de Desempenho

Resumo

Os testes de desempenho são cruciais para medir a qualidade dos desenvolvimentos de software, pois permitem identificar aspectos que precisam de ser melhorados a fim de alcançar a satisfação do cliente. O objectivo deste trabalho era identificar a técnica óptima de Machine Learning para prever se um desenvolvimento de software satisfaz ou não os critérios de aceitação do cliente. Foi utilizada uma base de dados de informações obtidas a partir de testes de desempenho de serviços web e a métrica de qualidade F1-score. Conclui-se que, embora a técnica da Random Forest tenha obtido a melhor pontuação, não é correcto dizer que é a melhor técnica de Aprendizagem Automática; a quantidade e qualidade dos dados utilizados na formação desempenham um papel muito importante, bem como um processamento adequado da informação.

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