Software Defect Prediction Based On Spiking Neural Networks

With the rapid development of science and technology,the complexity and scale of software have increased significantly.At the same time,the probability of defects in the software development stage has also increased.Defects can bring lot of bad effects:From running error to endangering customer's safety.It plays an important role in software testing resources,development cost and software quality to reduce software risk through defect prediction method before software use.Nowadays,software defect prediction technique has become one of the research hotspots in the field of software engineering.It purpose is to predict the software modules prone to defects before defects are found.Software defect prediction can be used to better determine the priority of software quality assurance work.Spiking neural networks use spike-based temporally encode.It is suitable for the research and analysis of brain nerve signals,It can process complex spatiotemporal information effectively.In order to solve the problem of software defect prediction,a software defect prediction method based on spiking neural networks is proposed,The specific research as follows:(1)Aiming at the same project isomorphic software defect prediction problem,a defect prediction method based on spiking neural networks is proposed.Firstly,the feed-forward neural networks are used to construct the prediction model.The specific steps are as follows: after the source data is normalized,the data is encoded by linear coding,and each instance data is encoded into a spiking sequence corresponding to each input neuron,then the network learning is carried out,and finally the prediction result is output after decoding.The same project software defect prediction of spiking neural network is carried out on 28 projects of 5 data sets Compared with BP neural network model,the model of spiking neural network has good prediction performance.(2)Aiming at the problem of cross-project software defect prediction,this paper proposes a software defect prediction method of spiking neural networks based on transfer learning.Use transfer learning technology to process cross-project defect data.This makes the data distribution between the test sets and the training sets more similar,and solves the problem that the data is linearly inseparable.In addition,the cost sensitive learning technology is integrated into the process of transfer learning,and different label categories in the test set data are given different cost weights to solve the class imbalance problem of the data set.Experiments are carried out on commonly used data sets,and compared with different classifiers.The experimental results verify the effectiveness of spiking neural networks model for software defect prediction,and verify the advantages of spiking neural networks in heterogeneous software defect prediction.The experimental results can prove that the software defect prediction method based on spiking neural networks proposed in this paper has certain research significance and practical value.