Machine Learning Methods and Asymmetric Cost Function to Estimate Execution Effort of Software Testing

Abstract

Planning and scheduling of testing activities play an important role for any independent test team that performs tests for different software systems, developed by different development teams. This work studies the application of machine learning tools and variable selection tools to solve the problem of estimating the execution effort of functional tests. An analysis of the test execution process is developed and experiments are performed on two real databases. The main contributions of this paper are the approach of selecting the significant variables for database synthesis and the use of an artificial neural network trained with an asymmetric cost function.