Prediction of software reliability using feedforward and recurrent neural nets

The authors present an adaptive modeling approach based on connectionist networks and demonstrate how both feedforward and recurrent networks and various training regimes can be applied to predict software reliability. They make an empirical comparison between this new approach and five well-known software reliability growth prediction models using data sets from 14 different software projects. The results presented suggest that connectionist networks adapt well to different data sets and exhibit better overall long-term predictive accuracy than the analytic models. This observation is true not only for the aggregate data, but for each individual item of data as well. The connectionist approach offers a distinct advantage for software reliability modeling in that the model development is automatic if one uses a training algorithm such as the cascade correlation. Two important characteristics of connectionist models are easy construction of appropriate models and good adaptability towards different data sets (i.e., different software projects)