Yifeng Zhang

TL;DR: KPWE, a new software defect prediction framework that considers the feature extraction and class imbalance issues, is proposed, and the empirical study on 44 software projects indicate that KPWE is superior to the baseline methods in most cases.

TL;DR: A deep neural network is used with a new hybrid loss function that consists of a triplet loss to learn a more discriminative feature representation of the defect data and a weighted cross-entropy loss to remedy the imbalance issue.

TL;DR: This work performed a large-scale empirical study on 40 unsupervised models on an open-source dataset including 27 project versions with 3 types of features to explore the impacts of clustering-based models on defect prediction performance.

TL;DR: This work makes the first attempt to develop a cross project ICFR model to address the data scarcity problem by transferring the knowledge from external projects to the current project via utilizing a state-of-the-art Balanced Distribution Adaptation (BDA) based transfer learning method.

TL;DR: Current unsupervised anomaly detection methods are extended into a semi-supervised manner, simultaneously leveraging normal data and a limited amount of abnormal data.