EM algorithms for estimating software reliability based on masked data

Abstract

In this paper, the software reliability estimation from masked data is considered based on superposition nonhomogeneous Poisson process models. The masked data are the system failure data when the exact causes of the failures, i.e., the components that have caused the system failure, may be unknown. The components of a software system may indicate its modules, testing strategies and the types of errors according to the practical situations. In general, the maximum likelihood estimates of parameters are difficult to find when there exist masked data, because the superposition process cannot be decomposed into the ordinary processes. In this study, the EM algorithm is investigated to solve the problem of maximum likelihood estimation. It is shown that the EM algorithm is powerful to deal with the masked data. By applying the EM algorithm, the masked data problem is simplified and is reduced to the common estimation problem without the masked data. This result makes it very easy to obtain maximum likelihood estimates of parameters.