Machine learning aplicado al análisis del rendimiento de desarrollos de software

Victor Daniel Gil-Vera; Cristian Seguro-Gallego

doi:10.33571/rpolitec.v18n35a9

Authors

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

Keywords:

Artificial Intelligence, Performance Analysis, Performance Testing, Predictive Modeling

Abstract

Performance tests are crucial to measure the quality of software developments, since they allow identifying aspects to be improved in order to achieve customer satisfaction. The objective of this research was to identify the optimal Machine Learning technique to predict whether or not a software development meets the customer's acceptance criteria. A dataset with information obtained from web services performance tests and the F1-score quality metric were used. This paper concludes that, although the Random Forest technique obtained the best score, it is not correct to state that it is the best Machine Learning technique; the quantity and quality of the data used in the training play a very important role, as well as an adequate processing of the information.

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