Randomly Selected Heterogenic Bagging with Cognitive Entity Metrics for Prediction of Heterogeneous Defects

doi:10.23940/ijpe.21.09.p6.796803

Abstract

Abstract: Software failure prediction is a supervised learning approach that plays an essential part in deciding the degree of software testing resources to be allocated. Due to data unavailability and the unbalanced nature of the data, other problems occur during this procedure. This study offers a model for heterogeneous prediction for transfer-learning samples of heterogeneous bagging. The concern of data not being available is handled using transfer learning and data balance and is done using the integrated sampling module. The approach suggested uses the design process for a replicated bag and the selection of cognitive metrics to increase prediction efficiency. Experiments demonstrate successful levels of prediction indicate increased performance compared to current literary work. The results showed a 19 percent increase in the overall forecasts, and a 25 percent decrease in the falsified prediction ratio, thus showing effective predictions.

Key words: mixed defect prediction, deep learning, sampling, bagging, data balance, behavioral metrics, software failure prediction

F. Leo John, Jose Prabhu Joseph John. Randomly Selected Heterogenic Bagging with Cognitive Entity Metrics for Prediction of Heterogeneous Defects [J]. Int J Performability Eng, 2021, 17(9): 796-803.

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