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An Empirical Study of Execution-Data Classification Based on Machine Learning

As it may be difficult for users to distinguish a passing execution from a failing execution for a released software system, researchers have proposed to apply the Random Forest algorithm to classify remotely-collected program execution data. In general, execution-data classification can be viewed as a machine-learning problem, in which a trained learner needs to classify whether each execution is a passing execution or a failing execution. In this paper, we report an empirical study that further investigates various issues in execution-data classification based on machine learning. Compared with previous research, our study further investigates the impact of the following issues: different machinelearning algorithms, the numbers of training instances to construct a classification model, and different types of exe-