Improving Software Effort Estimation Using Neuro-Fuzzy Model with SEER-SEM
This work addresses software project management challenges by providing a more accurate estimation method, though it appears incremental as it builds on existing techniques.
The researchers tackled software effort estimation by proposing a neuro-fuzzy model combined with SEER-SEM, which improved estimation accuracy compared to using SEER-SEM alone, as demonstrated with published and industrial data.
The aims of our research are to evaluate the prediction performance of the proposed neuro-fuzzy model with System Evaluation and Estimation of Resource Software Estimation Model (SEER-SEM) in software estimation practices and to apply the proposed architecture that combines the neuro-fuzzy technique with different algorithmic models. In this paper, an approach combining the neuro-fuzzy technique and the SEER-SEM effort estimation algorithm is described. This proposed model possesses positive characteristics such as learning ability, decreased sensitivity, effective generalization, and knowledge integration for introducing the neuro-fuzzy technique. Moreover, continuous rating values and linguistic values can be inputs of the proposed model for avoiding the large estimation deviation among similar projects. The performance of the proposed model is accessed by designing and conducting evaluation with published projects and industrial data. The evaluation results indicate that estimation with our proposed neuro-fuzzy model containing SEER-SEM is improved in comparison with the estimation results that only use SEER-SEM algorithm. At the same time, the results of this research also demonstrate that the general neuro-fuzzy framework can function with various algorithmic models for improving the performance of software effort estimation.