A Non-Parametric Software Reliability Modeling Approach by Using Gene Expression Programming
Haifeng Li, Minyan Lu, Min Zeng, Baiqiao Huang · 2012
Software reliability growth models (SRGMs) are very important for estimating and predicting software reliability. However, because the assumptions of traditional parame-tric SRGMs (PSRMs) are usually not consistent with the real conditions, the applicabil-ity and prediction accuracy of PSRMs are hence not very satisfying in most cases. In contrast to PSRMs, the non-parametric SRGMs (NPSRMs) which use machine learning (ML) techniques, such as artificial neural networks (ANN), support vector machine (SVM) and genetic programming (GP), for reliability modeling by mining failure data without any assumptions, can provide better prediction results across various projects. Gene Expression Programming (GEP) which is a new evolutionary algorithm based on Genetic algorithm (GA) and GP, has been acknowledged as a powerful ML and widely used in the field of data mining. Thus, we apply GEP into non-parametric software relia-bility modeling in this paper due to its unique and pretty characters, such as genetic en-coding method, translation process of chromosomes. This new GEP-based modeling ap-proach considers some important characters of reliability modeling in several main components of GEP, i.e. function set, terminal criteria, fitness function, and then obtains