Method of software defect prediction based on least absolute shrinkage and selection operator

Xuemei Hou; Fei Gao; Huanhuan Xu; Ruili Song

doi:10.1117/12.2660293

3 February 2023 Method of software defect prediction based on least absolute shrinkage and selection operator

Xuemei Hou, Fei Gao, Huanhuan Xu, Ruili Song

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111X (2023) https://doi.org/10.1117/12.2660293
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Software defect prediction is a means of software quality assurance, which aims to find potential defects in software through historical data and software characteristics. Feature selection is an important link in software defect prediction. With the rapid expansion of the number of features and the increase of feature dimensions, there may be multicollinearity problems between multiple types of features, which makes the model unstable and reduces the accuracy of the model. In order to solve the problem of multicollinearity between features, the least absolute shrinkage and selection operator algorithm is introduced into defect prediction. Through this algorithm, feature selection is realized, and the linear regression method is used for defect prediction, which improves the accuracy of classification results, reduces the over fitting of the model, and speeds up the convergence speed of the model.

Citation Download Citation

Xuemei Hou, Fei Gao, Huanhuan Xu, and Ruili Song "Method of software defect prediction based on least absolute shrinkage and selection operator", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111X (3 February 2023); https://doi.org/10.1117/12.2660293

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