To improve the accuracy of software defect prediction and the interpretability of prediction model,a software defect prediction model based on Spearman+DE+LIME+LightGBM(SDL-LighTGBM)ensemble learning was proposed.Spearman+LightGBM hybrid feature selection method was used to determine the optimal feature subset,and model complexity was reduced while the prediction performance of the model was guaranteed.The ensemble learning algorithm LightGBM was used to build a prediction model for feature subsets,and differential evolution(DE)algorithm was used to optimize the important hyperparame-ters of the model.Local interpretable model-agnostic accessibility(LIME)was used for locally interpretable analysis of the mo-del.Experimental results from 35 versions of 12 projects show that the proposed method is superior to the existing software defect prediction methods.The average increase of F1 value is 8.97％,the average increase of AUC value is 11.42％,and the model training time is shortened by 43.6％.
维普
万方数据