Software defect predicting method based on JCUDASA_BP algorithm

Abstract

The invention provides a software defect predicting method based on a JCUDASA_BP algorithm. The software defect predicting method based on JCUDASA_BP algorithm aims at solving the problems that an existing method based on an error back propagation algorithm is low in defect prediction accuracy and long in consumed time. The software defect predicting method based on the JCUDASA_BP algorithm comprises the first step of building a BP network, initializing weights of all layers in the BP network, determining the number of network input and output nodes, determining hidden nodes, determining the initialized weights and finishing initialization of the BP network structure, wherein the network comprises an input layer, a hidden layer and an output layer; the second step of counting the number of input samples according to the BP network structure built in the first step, achieving computation of layer output in a GPU through a starting thread through the JCUDA technology according to the input sample conditions, and computing an error value according to the output value and an expected error; the third step of computing whether the current error is received or not through a simulated annealing algorithm after the error between the output value and the expected value is counted, ending the process if the current error is received, otherwise continuing to adjust the network weights, and each sample is processed in a slitting mode.

Description

technical field [0001] The invention belongs to the technical field of distribution prediction in static prediction, and relates to a software defect prediction method based on JCUDASA_BP algorithm. Background technique [0002] Explanation of terms: [0003] The artificial neural network (ANN) model originated from the research and imitation of biological systems. The artificial neural network abstracts the human brain nerves from the perspective of information processing and mathematical and physical methods, and establishes a simplified model to simulate the intelligent processing behavior of the human brain. Artificial neural networks can be divided into two categories, feed-forward networks and feed-back networks. Each node in the feed-back network topology has a feedback mechanism, and the information processing of this network is a dynamic idea. Each node in the feedforward network receives the input of the previous level and outputs to the next layer of network. T...

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Problems solved by technology

[0008] The present invention aims at the problem of low defect prediction accuracy and too long time consumption based on the existing Error Back Propagation algorithm (Error Back Propagation, referred to as BP) method, and proposes an improved BP algorithm, that is, software defect prediction based on the JCUDASA_BP algorithm method, its prediction results are more accurate and faster than the original method (BP algorithm)

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