Automated software bug localization enabled by meta-heuristic-based convolutional neural network and improved deep neural network

Abstract

Automatically localizing bugs is implemented to maximize the speed of the bug localizing process and minimize the developer’s burdens. However, the existing automatic bug localization approaches have not fully used the inter-relations and the intra-relations of the source files and bug reports. In this paper, we present a CDNN-based software localization model, an automatic bug localization model enabled by a Meta-heuristic-based Convolutional Neural Network, and an improved Deep Neural Network, to enhance the effectiveness of the bug localization. First, the bug features are extracted by using word embedding technologies. The hybrid Meta heuristic-based Convolutional Neural Network (HM-CNN) is developed for feature optimization, as it can guarantee reliable localization by selecting the most significant features. The hybrid Meta heuristic-based Deep Neural Network (HM-DNN) is implemented for bug localization by considering objective functions related to the error difference between the actual score and the predicted score. Here, a new combined Grey Wolf-Sun Flower Optimization (HGW-SFO) is adopted for improving the efficiency of the system. Both the CNN in feature extraction (weighted CNN optimization) and DNN in localization phases (hidden neuron optimization) are improved by Hybrid HGW-SFO. Experimental results demonstrate that the proposed CDNN-based software bug localization model is significantly superior to the other conventional methods over several benchmark datasets in locating the bug source files. From the result analysis, while considering the accuracy value of the proposed HGW-SFO-CDNN, the proposed HGW-SFO-CDNN method secures 5.8%, 5.8%, and 9.75% more advanced performance than CNN, DNN, and CDNN at the number of retrieved files as 20.