# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 5 Conclusions and Prospects  
This paper proposed a cross-project defect prediction method based on manifold feature transformation, which can avoid feature distortion in the original feature space and effectively reduce the difference in data distribution between the source project and the target project in the manifold space. Experimental research shows that our method can alleviate the feature distortion in the original feature space, and especially when the feature dimension is large, it can improve the performance of cross-project defect prediction. A question worth discussing is why our method achieves better performance, especially when compared with the feature transformation and migration methods such as TCA+. As the results of previous experiments have shown, performing feature transformation and migration in the original feature space may not be able to capture the potential public space. It is more suitable for transformation and migration in the manifold space when the data feature dimension is high. Therefore, our method of manifold feature transformation is more effective in this case. At present, some artificial intelligence algorithms and their applications are also developing very rapidly, such as online learning algorithms [48], multi-objective optimization algorithms [49], related heuristic algorithms [50], etc. Therefore, CPDP research does not stop at using the feature transformation or feature migration methods mentioned in this article. The above is also worthy of being introduced into different scenarios of CPDP research. For example, online learning algorithms are combined with just-in-time software defect prediction [51], training models are obtained through dynamic incremental methods, multi-objective optimization and some heuristic algorithms are used for feature selection and instance selection [52], some data classification algorithms are used to enhance the effectiveness of defect prediction [53], etc. There are still many future steps worth noting in this paper. Firstly, the feature dimension of the original feature space transformed to the manifold space was not automatically selected. The later stage can focus on the automatic selection of the optimal parameters. Secondly, the distribution difference between the source project and the target project in the manifold space can be adapted from two aspects--edge distribution and conditional distribution, which were not considered in this paper. Thirdly, in the experiment, we chose two datasets of software projects, and our method can further extend to other suitable datasets in later periods. Finally, different classifiers have a certain impact on the method, and relevant research shows that ensemble learning can significantly improve the perfor  
Figure 5: F1 values obtained by different classifiers on the AEEEM dataset. mance of the method. In the future, we can study the impact of ensemble learning on the performance of the cross-project defect prediction method. Conceptualization, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); methodology, Y.Z. ([PERSON]); software, Y.Z. ([PERSON]) and Q.Y. ; validation, Y.Z. ([PERSON]), Y.Z. ([PERSON]) and X.C. ; formal analysis, Y.Z. ([PERSON]); investigation, Y.Z. ([PERSON]); resources, Y.Z. ([PERSON]); data curation, Y.Z. ([PERSON]); writing--original draft preparation, Y.Z. ([PERSON]); writing--review and editing, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); visualization, Y.Z. ([PERSON]); supervision, Q.Y. ; project administration, X.C. ; funding acquisition, Y.Z. ([PERSON]), Y.Z. ([PERSON]), Q.Y., and X.C. All authors have read and agreed to the published version of the manuscript.