In order to assess the model accuracy, it is necessary to use some quantitative measures of learning. In this study the Mean Squared Error (MSE) and regression analysis were used to evaluate the model performance. MSE is a useful measure of success for numeric prediction and is calculated using Eq. (1). It is worth mentioning that small values of MSE indicate better performance of the ANN model. It was found that the optimum performance of the model is at 25 neurons with MSE 0.1481. The accuracy of the model with different neurons is shown in Table 5.

MSE=(∑_(j=0)^P▒∑_(i=0)^N▒〖(t_ij-y_ij)〗^2 )/NP (5)

Where P is the number of output possessing elements, N is the

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One project was selected for the case study. One of the problems of the selected project is that the techniques selection procedures are based on elicitor experience. The aim of this case study is to check whether the proposed ANN model predicts more effective techniques than the current requirements engineering methods. The contextual situation of the selected project, that is, the particular values for the influential attributes is shown in Table 7. Table 8 shows the normalized values calculated using Eq. (4) for each attribute. These normalized values are considered the input data for the ANN

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To do this, a literature review was conducted to identify the attributes which are relevant to the context of the elicitation process and influence techniques selection. Then, these candidate attributes were analyzed using a multiple regression model to find the most important attributes influencing techniques selection and eliminate the less critical ones. Finally, a neural network based model for selection of elicitation techniques was developed. In order to empirically validate our ANN model a case study was conducted. The results showed that the proposed ANN model has proven its effectiveness in predicting more effective techniques than the current requirements engineering methods. It is recommended as a future work to integrate other machine learning techniques such as fuzzy logic with the proposed ANN model in order to enhance the model