  Software effort estimation is a critical part of software engineering.
Although many techniques and algorithmic models have been developed and
implemented by practitioners, accurate software effort prediction is still a
challenging endeavor. In order to address this issue, a novel soft computing
framework was previously developed. Our study utilizes this novel framework to
develop an approach combining the neuro-fuzzy technique with the System
Evaluation and Estimation of Resource - Software Estimation Model (SEER-SEM).
Moreover, our study assesses the performance of the proposed model by designing
and conducting evaluation with published industrial project data. After
analyzing the performance of our model in comparison to the SEER-SEM effort
estimation model alone, the proposed model demonstrates the ability of
improving the estimation accuracy, especially in its ability to reduce the
large Mean Relative Error (MRE). Furthermore, the results of this research
indicate that the general neuro-fuzzy framework can work with various
algorithmic models for improving the performance of software effort estimation.
