Abstract: To solve the problem of weak adaptability of single software reliability models and poor stability of data-driven models, this paper chooses three typical software reliability models as basic models, uses extreme learning machine to weigh and optimize the prediction results of basic models to obtain the combined software reliability model, which realizes the organic combination of classical software reliability models and artificial intelligence algorithm. Through simulation experiments on three sets of software failure data and comparison with the prediction results of single models, combinational models based on other neural network algorithms and data-driven model, it is verified that the combined software reliability model in this paper can effectively improve the prediction accuracy and model adaptability.

Key words: software reliability models, combinational models, neural network, extreme learning machine, prediction accuracy