When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors

arXiv:2211.05920v2 Announce Type: replace-cross
Abstract: Labeling a module defective or non-defective is an expensive task. Hence, there are often limits on how much-labeled data is available for training. Semi-supervised classifiers use far fewer labels for training models. However, there are numerous semi-supervised methods, including self-labeling, co-training, maximal-margin, and graph-based methods, to name a few. Only a handful of these methods have been tested in SE for (e.g.) predicting defects and even there, those methods have been tested on just a …

abstract arxiv classifiers cs.lg cs.se data labeling labels semi-supervised software training training models type value