Software quality prediction for high-assurance network telecommunications systems

Modern high-assurance network telecommunications systems! often must have high software reliability. Software quality models yield timely predictions of quality indicators on a module-by-module basis, enabling one to target enhancement techniques. This paper introduces fuzzy nonlinear regression (FNR) to practitioners in high-assurance systems engineering. FNR integrates fuzzy logic and neural networks techniques to generate good results. We present an innovative method that differs from other FNRs such that the statistics of the dependent variable are used to build the FNR instead of focusing on the cost function. We tested our model in a large network telecommunications system written in a Pascal-like proprietary language. Data splitting was applied in the case study. In order to avoid the unfair partition of the data set, we randomly split the data thirty times and performed the experiment for each iteration. We also conducted our experiment using multiple linear regression (MLR) and we found that the results of FNR were significantly better than those using MLR.