Cross-Project Dynamic Defect Prediction Model for Crowdsourced test
Yi Yao, Yuchan Liu, Song Huang, Hao Chen, Jialuo Liu, Fan Yang · 2020
By comparing the predicted number of defects with the number found in crowdsourced test in real time, people can dynamically assess the progress of crowdsourced test tasks. In this paper, we propose a cross-project dynamic defect prediction model (CPDDPM) for crowdsourced test to predict the number of defects in real time. In the construction of training dataset, we use density-based clustering method to select instances from the multiple source project datasets and build the initial training dataset. In the dynamic correction, CPDDPM iteratively corrects the prediction model using crowdsourced test reports and ability attributes of the crowdsourced testers until the predicted results converge. We collected project defect datasets on the crowdsourced test platform, and evaluated prediction accuracy of CPDDPM by using relative error and prediction at level l. The results show that CPDDPM can greatly improve the prediction performance of defect number.