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Identifying bug immediately when it is introduced can help improve the validity and effectiveness of bug fixing. Predicting bugs in software code changes makes such identification possible. Buggy changes, changes that introduce bugs into source code, can be viewed as anomalies relative to clean changes for that they are rare and irregular. Thus, anomaly detection techniques can be applied to buggy...
Incremental Attribute Learning (IAL) is a feasible machine learning strategy for solving high-dimensional pattern classification problems. It gradually trains features one by one, which is quite different from those conventional machine learning approaches where features are trained in one batch. Preprocessing, such as feature selection, feature ordering and feature extraction, has been verified as...
Feature Extraction (FE) based on Principal Component Analysis (PCA) can effectively improve classification results by reducing the interference among features. However, such a good method has not been employed in previous studies of Incremental Attribute Learning (IAL), a novel machine learning strategy, where features are gradually trained one by one in order to remove interference among features...
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