The explicit mean value function solutions for the proposed models are presented.
Expected number of software failures detected by time t, also known as mean value function.
end{aligned} (8)Table 1 summarizes the features and mean value function of the proposed model and existing models.
end{aligned} (1)However, nowadays, the above software mean value function is not adequate to describe more complex software product.
The explicit mean value function solution of the proposed model for a logistic fault-detection rate is presented.
Once the analytical expression for the mean value function (m(t)) is derived, the model parameters to be estimated in the mean value function can be obtained with a help of developed Matlab programs that based on the least square estimate (LSE) method.
The explicit solution of the mean value functions for these new models are derived in Sect. 2.
end{aligned} (5)Table 1 summarizes the proposed model and several existing well-known NHPP models with different mean value functions.
Substitute the function (c(t)) into Eq. (7), we can easily obtain the expected number of software failures detected by time t with random environments: begin{aligned} m(t)=Nleft( {1-left( {frac{beta }{beta +a^{t^b}-1}}right) ^alpha }right) end{aligned} (9)Table 1 summarizes the two proposed models and several existing well-known NHPP models with different mean value functions.
Many existing NHPP software reliability models [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26] have been carried out through the fault intensity rate function and the mean value functions (m(t)) within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of software.
Many existing NHPP software reliability models [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28] have been used through the fault intensity rate function and the mean value functions m(t) within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of the software.
