Weighted standardized optimization selection software reliability model method
A technology for optimal selection and reliability, applied in software testing/debugging, hardware monitoring, instruments, etc., to achieve simple and effective results
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Embodiment 1
[0040] Selection of Software Reliability Model
[0041] In order to verify the effectiveness of the proposed optimization method, 13 traditional classic software reliability models are used to conduct relevant experiments.
[0042]13 traditional classic software reliability models include Goel-Okumto (G-O) model, Delayed S-shaped (DSS) model, Inflection S-shaped (ISS) model, Generalized Goel (GGO) model, ModifiedDuane (MD) model, logistic Growth (LG) model, Musa-Okumoto (MO) model, Yamada Imperfect debugging model 1 (YIDM1), Yamada Imperfect debugging model 2 (YIDM2), Kapur 1 model, P-N-Z model, P-Z model, and Zhang-Teng-Pham model. Among them, there are 7 perfect debugging models, Goel-Okumto (G-O) model, Delayed S-shaped (DSS) model, Inflection S-shaped (ISS) model, Generalized Goel (GGO) model, Modified Duane (MD) model, logistic Growth (LG) model and Musa-Okumoto (MO) model. Imperfect debugging models include 6, Yamada Imperfect debugging model 1 (YIDM1), YamadaImperfect...
Embodiment 2
[0048] Software Reliability Model Evaluation Criteria
[0049] In order to quantify the evaluation performance of software reliability models, this paper gives 10 evaluation criteria of software reliability models. These 10 software reliability model evaluation criteria evaluate the performance of software reliability models from different aspects. It should be noted that this paper takes 10 software reliability model evaluation criteria as 10 attributes of software reliability model. The evaluation criteria of these 10 software reliability models include, Bias, MSE, MAE, MEOP, PRR, Variance, RMSPE, R 2 , SSE and TS. Among them, the smaller the value of Bias, MSE, MAE, MEOP, PRR, Variance, RMSPE, SSE and TS, the better the performance of the software reliability model. R 2 The larger the value of , the better the fitting performance of the software reliability model. Table 2 gives a detailed description of the 10 software reliability model evaluation criteria.
[0050] T...
Embodiment 3
[0054] Selecting the Optimal Software Reliability Model
[0055] In general, the maximum likelihood estimation method is better than the least squares method in the case of large samples. But in the case of small samples, the least squares method is better than the maximum likelihood estimation method. Because the failure data set collected by software testing is a small sample situation, and the parameter value of the model is estimated by the maximum likelihood estimation method, in some cases there is no maximum likelihood function value. Therefore, in order to facilitate the comparison and selection of models, this paper uses the least square method to estimate the parameter values of the software reliability model.
[0056] (1) Software fault data set
[0057] To verify the effectiveness of the proposed method, we conduct corresponding experiments with two fault datasets collected from actual software tests. The first software fault dataset was detected and collected...
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