An optimized method for software reliability model based on nonhomogeneous Poisson process

The present study proposes an optimized novel approach to improve the software reliability model based on the nonhomogeneous Poisson process (NHPP). The approach repeatedly implements the function with exponential distribution to fit a logarithmic difference between the estimated values and observed values from a software historical fault data set. Moreover, the logarithmic difference of the values gradually tends to be zero with more fittings. Furthermore, the trend in which their logarithmic difference essentially converges to a stable value over time contributes to building a fitting model and predicting the number of remaining faults in software testing. The optimal solutions are given in an optimized process. Experimental results show that the proposed optimized models fit the historical fault data set better, and more accurately predict the remaining number of faults than the traditional models based on NHPP in the software testing process.