Download PDFOpen PDF in browserImproving Test Case Selection by Handling Class and Attribute NoiseEasyChair Preprint 660544 pages•Date: September 13, 2021AbstractBig data and machine learning models have been increasingly used to support software engineering practices. One example is the use of machine learning models to improve test case selection in continuous integration. However, one of the challenges in building such models is the large volume of noise that comes in data, which impedes their predictive performances. In this paper, we address this issue by studying the effect of two types of noise (class and attribute) on the predictive performance of a test selection model. For this purpose, we analyze the effect of class noise by using an approach that relies on domain knowledge for relabeling contradictory entries and removing duplicate ones. Thereafter, an existing approach from the literature is used to experimentally study the effect of attribute noise removal on learning. The analysis results show that the best learning is achieved when training a model on class-noise cleaned data only -- irrespective of attribute noise. Keyphrases: attribute noise, class noise, machine learning, regression testing, test case selection
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