Cross-Project Defect Prediction based on Cognitive Metrics Using Sampled Boosting
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Abstract
Software defect prediction in software components is mandatory to provide reliable software components, failing of which might lead to disastrous consequences. Lack of sufficient training data and presence of imbalance in the data tends to create challenges in developing an effective automated software defect prediction module. This work proposes an effective ensemble model aimed to handle the challenges in the defect prediction process. The proposed model operates on cross project data to generate a training model. Further, generation of cognitive metrics has proved to improve the prediction process. The boosted ensures reduction in bias due to imbalance, hence ensuring effective predictions, which are evident in the experimental results.
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Dr. T. N. Ravi, N. V. (2021). Cross-Project Defect Prediction based on Cognitive Metrics Using Sampled Boosting. Annals of the Romanian Society for Cell Biology, 25(6), 7431–7440. Retrieved from https://www.annalsofrscb.ro/index.php/journal/article/view/6893
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