# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 2 Related Work  
In recent years, CPDP research has attracted widespread attention from software engineering researchers. [PERSON] et al. [PERSON] et al. (2018) conducted a feasibility study on CPDP for the first time and discussed whether CPDP is worth studying. Finally, they found that CPDP research is very valuable and very challenging. Subsequently, [PERSON] et al. [PERSON] et al. (2019) conducted a large-scale experimental study on CPDP research and found that most of the experimental results had poor performance, and only a few CPDP experiments achieved satisfactory performance on a variety of indicators. Since then, researchers have proposed various methods to improve the performance of CPDP from different angles. Some researchers choose suitable training data for the target project based on the similarity measure. [PERSON] et al. [PERSON] et al. (2017) and [PERSON] et al. [PERSON] et al. (2018) calculated the values of the features of each project on a variety of statistical indicators, such as mode, average, variance, etc., constructed a new distribution vector, and then selected the most similar source project for the target project as the training data by calculating the difference of each distribution vector. [PERSON] et al. [PERSON] et al. (2019) proposed a Burak filtering method based on target project instances, which, in turn, selected the most similar k instances from candidate source projects for each instance in the target project to obtain training data. [PERSON] et al. [PERSON] et al. (2019) proposed a similar instance filtering method. The difference is that [PERSON] et al. used a clustering algorithm to select training data from source project instances. Some researchers solve the problem of data distribution differences between projects from the perspective of feature transformation and feature migration. [PERSON] et al. [PERSON] et al. (2019) proposed a method TCA+ based on feature transformation and migration; TCA+ defines some standardization rules first, selects the best data standardization strategy, and then performs feature migration on CPDP to make the feature distribution of different projects similar. It also improves the performance of CPDP. [PERSON] et al. [PERSON] et al. (2019) proposed a TNB method based on instance weighting and transfer learning, which sets weights and migrates instances in candidate source projects by predicting the distribution of target project data and improves the performance of CPDP. [PERSON] et al. analyzed the importance of features and instances in cross-project defect prediction methods [PERSON] et al. (2019), conducted a lot of empirical research on feature selection methods [PERSON] et al. (2019), and proposed a feature matching and migration cross-project defect prediction method [PERSON] et al. (2019). Recently, researchers have turned to semi-supervised cross-project defect prediction methods. [PERSON] et al. [PERSON] et al. (2019) studied the problem of semi-supervised cross-project defect prediction and proposed a cost-sensitive kernel semi-supervised dictionary learning method (CKSDL). They introduced semi-supervised dictionary learning into the field of software defect prediction. CKSDL uses kernel mapping to enhance the separability and cost-sensitive technique to ease the misclassification problem. [PERSON] et al. [PERSON] et al. (2019) proposed a cross-project defect prediction method based on feature migration and instance migration. Specifically, in the feature migration stage, the method uses cluster analysis to select the features with high distribution similarity between the source project and the target project; in the instance migration stage, based on the TrAdaBoost method, this method selects instances with similar distribution to these labeled instances from the source project with the help of a small number of labeled instances in the target project. In addition, [PERSON] et al. [PERSON] et al. (2019) proposed a cost-sensitive transfer kernel method for linear inseparability and class imbalance in CPDP.