Software bug localization based on optimized and ensembled deep learning models

An automated task for finding the essential buggy files among software projects with the help of a given bug report is termed bug localization. The conventional approaches suffer from the challenges of performing lexical matching. Particularly, the terms utilized for describing the bugs in the bug reports are observed to be irrelevant to the terms used in the source code files. To resolve these problems, we propose an optimized and ensemble deep learning model for software bug localization. These features are reduced by the principle component analysis (PCA). Then, they are selected by the weighted convolutional neural network (CNN) model with the support of the Modified Scatter Probability-based Coyote Optimization Algorithm (MSP-COA). Finally, the optimal features are subjected to the ensemble deep neural network and long short-term memory (DNN-LSTM), with parameter tuning by the MSP-COA. Experimental results show that the proposed approach can achieve higher bug localization accuracy than individual models. We present an optimized ensemble deep learning model for software bug localization. Features are reduced via PCA and selected using a C-CNN model aided by the Modified Scatter Probability-based Coyote Optimization Algorithm. Results demonstrate superior bug localization accuracy compared to standalone models. image