Improving the Performance of Neuro-Fuzzy Function Point Backfiring Model with Additional Environmental Factors
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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 was found that the original NFFPB model out performs the extended model. The results were investigated and explained to why the extended model performed worse.