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Software-defect prediction within and across projects based on improved self-organizing data mining

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Abstract

This paper proposes a new method for software-defect prediction based on self-organizing data mining; this method can establish a causal relationship between software metrics and defects. Defect-prediction models were established for intra-project and cross-project scenarios. For intra-project forecasting, this article establishes a self-organizing data mining model, adding a method of smooth data preprocessing to solve the problem of data imbalance. For cross-project forecasting, this article establishes a self-organizing data mining model, solves the difference between the two by finding a source-project instance with a larger correlation coefficient with the target project, and establishes a defect-prediction model for the selected source-project instance. This paper aims to achieve classification and ranking prediction. The proposed method is tested on public-defect datasets. In the classification-prediction experiment, the precision, F-measure, and AUC evaluation indicators of this method are used. In the ranking-prediction experiment, AAE and ARE evaluation by this method are optimized. The algorithm is found to be an efficient and feasible method for software-defect prediction.

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Acknowledgements

This work was supported by the National Engineering Laboratory Project for the Safety Technology of Urban Rail Transit System (Development and Reform Office High Technology [2016] No. 583).

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Authors and Affiliations

  1. College of Engineering, China Agricultural University, Beijing, 100083, People’s Republic of China

    Qing Zhang

  2. SinoRail Network Technology Research Institute, Beijing, 100044, People’s Republic of China

    Junhua Ren

  3. China Railway Information Technology Co. Ltd, Beijing, 100038, People’s Republic of China

    Junhua Ren

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  1. Qing Zhang
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Correspondence to Qing Zhang.

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Zhang, Q., Ren, J. Software-defect prediction within and across projects based on improved self-organizing data mining. J Supercomput 78, 6147–6173 (2022). https://doi.org/10.1007/s11227-021-04113-8

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