A Genetic Programming Approach for Software Reliability Modeling
Genetic Programming ( GP) models adapt better to the reliability curve when compared with other traditional, and non-parametric models. In a previous work, we conducted experiments with models based on time, and on coverage. We introduced an approach, named Genetic Programming and Boosting (GPB), that uses boosting techniques to improve the performance of GP. This approach presented better results than classical GP, but required ten times the number of executions. Therefore, we introduce in this paper a new GP based approach, named (mu + lambda) GP. To evaluate this new approach, we repeated the same experiments conducted before. The results obtained show that the (mu + lambda) GP approach presents the same cost of classical GP, and that there is no significant difference in the performance when compared with the GPB approach. Hence, it is an excellent, less expensive technique to model software reliability.
software reliability models fault prediction machine learning techniques