Defect Prediction Technology of Aerospace Software Based on Deep Neural Network and Process Measurement

Tianwen Yao, Ben Zhang, Jun Peng, Zhiqiang Han, Zhaobing Yang, Zhi Zhang, Bo Zhang and Antonio M. Gonçalves de Lima

Mathematical Problems in Engineering, 2022, vol. 2022, 1-8

Abstract: In order to ensure high reliability, the efficiency of traditional aerospace software testing is often low. With the rapid development of machine learning, its powerful data feature extraction ability has great potential in improving the efficiency of aerospace software testing. Therefore, this paper proposed a software defect prediction method based on deep neural network and process measurement. Based on the NASA data set and combined with the software process data, the software defect measurement set is constructed. 35 measurement elements are used as the original input, and multiple single-layer automatic coding networks are superimposed to form the deep neural network model of software defect. The model is finally trained by the layer-by-layer greedy training method to realize software defect prediction. Experimental verification shows that the prediction method has a good prediction effect on aerospace software defects, and the accuracy rate reached 90%, which can greatly improve the efficiency and effect of aerospace software testing.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1276830

DOI: 10.1155/2022/1276830

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