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- W4386973183 abstract "One of the most important characteristics of a software is its quality. Software defects are more likely to occur when software designs become more complicated in response to rising demand. Software quality is increased by testers by repairing defects. Consequently, the study of defects greatly raises the quality of software. Due to the increased amount of defects brought on by software complexity, manual defect detection can become an extremely time-consuming procedure. This encouraged researchers to create methods for the automatic detection of software defects. Due to the under/over fitting issues, existing methods for predicting software defects often have low accuracy (Matloob in IEEE Access 9:98754–98771, 2021). The use of machine learning is widespread in the realm of software defect prediction. The defect prediction effect of the single ML model isn’t optimal, according to the results of the available research. Hence, we suggest an ensemble learning method to resolve the issue, in which different machine learning algorithms are combined to produce an accurate defect prediction." @default.
- W4386973183 created "2023-09-23" @default.
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- W4386973183 date "2023-01-01" @default.
- W4386973183 modified "2023-09-29" @default.
- W4386973183 title "Using Ensemble of Different Classifiers for Defect Prediction" @default.
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- W4386973183 doi "https://doi.org/10.1007/978-981-99-3758-5_39" @default.
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