Content Management System Effort Estimation Using Bagging Predictors

This paper presents an effort estimation model for the content management systems which can be used to estimate the effort required for designing and developing. The data from the different content management system projects are studied and the model is finalized by using the bagging predictor on linear regression learning model. These projects are categorized based on their size and total/build effort ratio. The size of the project is estimated by using the modified object point analysis approach. A questionnaire is prepared to help project managers to find out the different objects, their categories and their complexity in the project. Final effort is estimated using the project size and the different adjustment factors. For better calculation of these adjustments factors, these are categorized based on their characteristics viz. Production and General system characteristics, Developer's experience and capability. Another questionnaire is used to refine the model and it has to be filled by the project managers after completing the project. The proposed model is validated by studying twelve completed projects taken from industry and seventy different projects completed by the students. The proposed model shows a great improvement as compared to the earlier models used in effort estimation of content management system projects.