Moth Flame Optimization Based FCNN for Prediction of Bugs in Software  

作　　者：C.Anjali Julia Punitha Malar Dhas J.Amar Pratap Singh 

机构地区：[1]Department of CSE,Noorul Islam Center for Higher Studies,629180,Tamil Nadu,India

出　　处：《Intelligent Automation & Soft Computing》2023年第5期1241-1256,共16页智能自动化与软计算（英文）

摘　　要：The software engineering technique makes it possible to create high-quality 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 proce-dures isfinding andfixing 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 Clas-sifier(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.

关 键 词：Faster convolution neural network Moth Flame Optimization(MFO) Support Vector Machine(SVM) AdaBoost(AB) software bug prediction 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]