Parameter estimation of hyper-geometric distribution software reliability growth model by genetic algorithms

Usually, parameters in software reliability growth models are not known, and they must be estimated by using observed failure data. Several estimation methods have been proposed, but most of them have restrictions such as the existence of derivatives on evaluation functions. On the other hand, genetic algorithms (GA) provide us with robust optimization methods in many fields. We apply GA to the parameter estimation of the hyper-geometric distribution software reliability growth model. Experimental result shows that GA is effective in the parameter estimation and removes restrictions from software reliability growth models.