Abstract
Traditional software defect prediction approaches often focus on static code metrics. Software evolution could cause changes to the source code of the software, as well as changes to the code metrics and label. In recent years, researchers have proposed many process metrics for evolutionary projects, but they are mainly used to predict the defect proneness of software modules. Whether the change of code metrics (CCMs) could be used to predict the label change on evolutionary projects, and which CCMs are more correlated to the label change? To answer these questions, this paper proposes a framework for building the new datasets with CCMs and label change, and explores the correlations between CCMs and label change with feature ranking approaches. An empirical study is conducted on 40 versions of 11 open-source projects. The experimental results indicate that CCMs can predict the label change.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (61902161 and 62077029), the CCF-Huawei Populus Grove Fund (CCF-HuaweiFM202209), the Guangxi Key Laboratory of Trusted Software (kx201704), and the Research Support Program for Doctorate Teachers of Jiangsu Normal University (17XLR001).
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Yu, Q., Jiang, S., Zhu, Y., Han, H., Zhao, Y., Jiang, Y. (2022). The Change of Code Metrics for Predicting the Label Change on Evolutionary Projects: An Empirical Study. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_27
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