Machine Learning applied to Software Testing

• Vinicius Melchior L. Santos UFAM
• Lidy Emanuelle M. Santos UFAM
• Odette M. Passos UFAM

DOI: https://doi.org/10.5753/connect.2025.12292

Abstract

Software testing is one of the main phases in guaranteeing the quality of a system, as it makes it possible not only to correct, but also to identify and prevent defects. Considering the constant evolution of technologies, there is a need to develop new ways of keeping tests effective and adaptable. In this context, Machine Learning applied to software testing has emerged, demonstrating new possibilities for validating quality. Thus, this rapid review sought to analyze relevant work in order to identify the main methods and tools, as well as the advantages and disadvantages of applying Machine Learning to software testing.

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