Discrete nonhomogeneous poisson process software reliability growth models based on test coverage

To incorporate the effect of test coverage, we proposed two novel discrete nonhomogeneous Poisson process software reliability growth models in this article using failure data and test coverage, which are both regarding the number of executed test cases instead of execution time. Because one of the most important factors of the coverage-based software reliability growth models is the test coverage function (TCF), we first discussed a discrete TCF based on beta function. Then we developed two discrete mean value functions (MVF) integrating test coverage and imperfect debugging. Finally, the proposed discrete TCF and MVFs are evaluated and validated on two actual software reliability data sets. The results of numerical illustration demonstrate that the proposed TCF and the MVFs provide better estimation and fitting under comparisons.