Nonlinear dynamic measurement method of software reliability based on data mining

Yinsheng Fu (), Jullius Kumar (), Bibhu Prasad Ganthia () and Rahul Neware ()
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Yinsheng Fu: Anyang Vocational and Technical College
Jullius Kumar: Dr. Rammanohar Lohia Avadh University
Bibhu Prasad Ganthia: IGIT Sarang
Rahul Neware: Høgskulen På Vestlandet

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 27, 273-280

Abstract: Abstract Developing high-quality software is the ultimate goal of any software development organization. But the major challenge is to achieve good quality. It can usually only be measured after delivery, and reliability is the primary measure of software quality. During development, there are many attempts to assess software quality. To solve the reliability problem of evaluating software, the data mining model of BP neural network is proposed to predict the reliability of software. Firstly, data mining is carried out on the number of faults of the software, and data such as the cumulative execution time and the corresponding observed cumulative number of faults in the testing process of the software within a set of specific times are collected. Secondly, the training model of BP neural network is built according to the failure data samples, and the software is trained and learned according to the historical data, it is used to test the cumulative execution time of the future stage, calculate the corresponding predicted cumulative failure number of the software, and then verify the reliability of the target software. The example proves that the BP neural network is more accurate in predicting the 17th, 18th, and 19th groups of cumulative failure times compared with the traditional nonlinear modes, Jelinski-Moranda model, Goel-Okumoto model and Yamada S-shaped model, the number of faults predicted is more the prediction accuracy is higher, and it is more suitable for application and reliability evaluation of software.

Keywords: Data mining; Software reliability; Nonlinear measurement; BP neural network; Jelinski-Moranda model (JM); Goel-Okumoto model (GO) (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01389-0

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