关键词: 软件可靠性建模, 基因表达式编程(GEP), 非参数化软件可靠性模型, 机器学习

Abstract: Gene expression programming (GEP), which is a new evolutionary algorithm based on genetic algorithm and genetic programming, has been acknowledged as a powerful machine learning technique and widely used in the field of data mining. Thus, this paper applies GEP into the non-parametric software reliability modeling due to its unique and pretty characters. This new GEP-based modeling approach considers some important characters of reli-ability modeling in several main components of GEP, i.e. function set, terminal criteria, fitness function, and then obtains the final model (named GEP-NPSRM) by training on the failure data. Finally, for several real failure data-sets, four case studies are proposed by respectively comparing GEP-NPSRM with several representative existing software reliability models in term of the fitting and prediction powers. The results show that the GEP-NPSRM pro-vides better fitting and prediction performances.

Key words: software reliability modeling, gene expression programming (GEP), non-parametric software reliability model, machine learning