Abstract
Cross-project software defect prediction solves the problem that traditional defect prediction can’t get enough data, but how to apply the model learned from the data of different mechanisms to the target data set is a new problem. At the same time, there is the problem that information redundancy in the training process leads to low accuracy. Based on the difference of projects, this paper uses MIC to filter features to solve the problem of information redundancy. At the same time, combined with the TrAdaboost algorithm, which is based on the idea of aggravating multiple classification error samples, this paper proposes a cross-project software prediction method based on feature selection and migration learning. Experimental results show that the algorithm proposed in this paper has better experimental results on AUC and F1.
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Acknowledgement
This work was supported by the National Key Research and Development Program of China under Grant 2016QY06X1205.
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Lei, T., Xue, J., Han, W. (2020). Cross-Project Software Defect Prediction Based on Feature Selection and Transfer Learning. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12488. Springer, Cham. https://doi.org/10.1007/978-3-030-62463-7_33
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DOI: https://doi.org/10.1007/978-3-030-62463-7_33
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