Improving the Performance of Neuro-Fuzzy Function Point Backfiring Model with Additional Environmental Factors
Justin Wong, Danny Ho, Luiz Fernando Capretz · Advances in computational intelligence and robotics book series · 2013
Backfiring is a technique used for estimating the size of source code based on function points and programming. In this study, additional software environmental parameters such as Function Point count standard, development environment, problem domain, and size are applied to the Neuro-Fuzzy Function Point Backfiring (NFFPB) model. The neural network and fuzzy logic designs are introduced for both models. Both estimation models are compared against the same data source of software projects. It is found that the original NFFPB model outperforms the extended model. The results are investigated, and it is explained why the extended model performed worse.