Adaptive multi-model synthesis dynamic prediction of software reliability based on particle swarm optimization

Different reliability model results in different reliability prediction for the same software, and failed process of the same software could not be described using a single model in software reliability project. Concerning these problems, an adaptive multi-model synthesis dynamic prediction method for software reliability based on particle swarm optimization (PSO) algorithm was proposed. This method can select the most adaptive history data and the best weight allocation for every sub-model to predict the next time software reliability automatically. It integrates the strong point of various models and makes in-homogeneity model compensating each other through dynamic changing their weights adaptively and discards outdated data automatically. This method can solve the practical problems in software engineering. A case study was done to demonstrate the feasibility of this method and compare with basic particle swarm optimization.