Modeling software effort estimation using hybrid PSO-ANFIS

Accurate estimating software development effort is essential in effective project management processes such as budgeting, project planning and control. To achieve an accurate estimate some algorithmic estimation techniques proposed to eliminate or reduce inaccuracies estimation. COCOMO is a parametric model used to estimate software effort. However, so far no model has proven successful to effectively and consistently predict software effort. Parametric models are considered vulnerable when faced with the problem of non-linearity of the complex in the parameters. In recent years, some estimation technique appears using intelligent systems to predict software effort. This study uses a model Neuro-fuzzy optimized with PSO to get the right model to improve the estimation effort at NASA dataset software project. Parameter cost driver, consisting of 17 feature COCOMO will then be optimized using PSO techniques to get a better prediction accuracy. Furthermore, the results of the optimization will be trained in using the algorithm to get a prediction Neuro-fuzzy effort. The performance of the proposed estimation model will be evaluated with some other intelligent system model parameters to evaluate several criteria such as Mean Standard Error (MSE), Mean Magnitude of Relative Error (MMER), and Level Prediction (Pred). The model that best shows the error rate MSE and MMER lowest to highest Pred.