Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

Abstract

The software engineering technique makes it possible to create highquality software. One of the most significant qualities of good software is that it is devoid of bugs. One of the most time-consuming and costly software procedures is finding and fixing bugs. Although it is impossible to eradicate all bugs, it is feasible to reduce the number of bugs and their negative effects. To broaden the scope of bug prediction techniques and increase software quality, numerous causes of software problems must be identified, and successful bug prediction models must be implemented. This study employs a hybrid of Faster Convolution Neural Network and the Moth Flame Optimization (MFO) algorithm to forecast the number of bugs in software based on the program data itself, such as the line quantity in codes, methods characteristics, and other essential software aspects. Here, the MFO method is used to train the neural network to identify optimal weights. The proposed MFO-FCNN technique is compared with existing methods such as AdaBoost (AB), Random Forest (RF), K-Nearest Neighbour (KNN), K-Means Clustering (KMC), Support Vector Machine (SVM) and Bagging Classifier (BC) are examples of machine learning (ML) techniques. The assessment method revealed that machine learning techniques may be employed successfully and through a high level of accuracy. The obtained data revealed that the proposed strategy outperforms the traditional approach.