Software reliability models are used to estimate the probability of a software fails along the time. They are fundamental to plan test activities and to ensure the quality of the software being developed. Two kind of models are generally used: time or test coverage based models. In our previous work, we successfully explored Genetic Programming (GP) to derive reliability models. However, nowadays Boosting techniques (BT) have been successfully applied with other Machine Learning techniques, including GP. BT merge several hypotheses of the training set to get better results. With the goal of improving the GP software reliability models, this work explores the combination GP and BT. The results show advantages in the use of the proposed approach.