Software Defect Prediction by Online Learning Considering Defect Overlooking
Software Engineering
2023-08-29 v1
Abstract
Building defect prediction models based on online learning can enhance prediction accuracy. It continuously rebuilds a new prediction model when adding a new data point. However, predicting a module as "non-defective" (i.e., negative prediction) can result in fewer test cases for such modules. Therefore, defects can be overlooked during testing, even when the module is defective. The erroneous test results are used as learning data by online learning, which could negatively affect prediction accuracy. In our experiment, we demonstrate this negative influence on prediction accuracy.
Cite
@article{arxiv.2308.13582,
title = {Software Defect Prediction by Online Learning Considering Defect Overlooking},
author = {Yuta Yamasaki and Nikolay Fedorov and Masateru Tsunoda and Akito Monden and Amjed Tahir and Kwabena Ebo Bennin and Koji Toda and Keitaro Nakasai},
journal= {arXiv preprint arXiv:2308.13582},
year = {2023}
}
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2 pages