Abstract
Accurate software development effort estimation is critical to the success of software projects. Altho ugh many techniques and algorithmic models have been developed and implemented by practitioners, accurate software development effort prediction is still a c hallenging endeavor in the field of software engine ering, especially in handling uncertain and imprecise inpu ts and collinear characteristics. In this study, a hybrid intelligent model combining a neural network model integrated with fuzzy model (neuro-fuzzy model) has been used to improve the accuracy of estimating sof tware cost. The performance of the proposed model i s assessed by designing and conducting evaluation wit h published project and industrial data. Results ha ve shown that the proposed model demonstrates the ability of improving the estimation accuracy by 18% bas ed on the Mean Magnitude of Relative Error (MMRE) criterion.
Highlights
Results have shown that the proposed model demonstrates the ability of improving the estimation accuracy by 18% based on the Mean Magnitude of Relative Error (MMRE) criterion
This includes (Huang et al, 2007; 2004) who used a neuro-fuzzy model to improve the accuracy of the COCOMO Model, other work such as (Nassif et al, 2013) and (Nassif et al, 2011) have been used to improve the accuracy of the Use Case Point Model using Machine Learning techniques and (Du et al, 2010) who used a neural network with fuzzy logic model to improve the SEER-SEM algorithm; the evaluation conducted in the latter work was poor
Experiments have shown that our model surpasses the SEER-SEM model by 18% based on the Mean Magnitude of Relative Error (MMRE) criterion
Summary
Various factors affect software effort estimation in organizations and projects, including inconsistent software processes and measurement definitions in projects, substantial diversity among projects and extreme differences in product sizes These situations create challenges in the practice of software effort estimation, making it difficult to yield a high modeling accuracy affects the quality of estimation. These studies are aimed at improving the predictive performance of current models by introducing new techniques and methodologies.
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