Electrical & Computer Engineering

Accurate software development cost estimation is important for effective project management such as budgeting, project planning and control. So far, no model has proved to be successful at effectively and consistently predicting software development cost. A novel neuro-fuzzy Constructive Cost Model (COCOMO) is proposed for software cost estimation. This model carries some of the desirable features of a neuro-fuzzy approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural network approach, the proposed model can be interpreted and validated by experts, and has good generalization capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. In addition, it allows input to have continuous rating values and linguistic values, thus avoiding the problem of similar projects having large different estimated costs. A detailed learning algorithm is also presented in this work. The validation using industry project data shows that the model greatly improves estimation accuracy in comparison with the well-known COCOMO model. #

Software projects development can be considered the most uncertain and complex project when compared to other types of projects, because its activities involve an intangible product and is continually changing in response to customers' requirements and the development of new technology. Under these conditions, risk management in software development projects is mandatory, but often it is difficult and expensive to implement. Expert COCOMO is one efficient approach to software project risk management, which leverages existing knowledge and expertise from previous effort estimation activities to assess the risk in a new software project. However, this method has a limitation because it cannot effectively deal with imprecise and uncertain inputs in the form of linguistic terms such as: Very

A software development project is considered to be risky due to the uncertainty of the information (customer requirements), the complexity of the process, and the intangible nature of the product. Under these conditions, risk management in software development projects is mandatory, but often it is difficult and expensive to implement. Expert COCOMO is an efficient approach to software project risk management, which leverages existing knowledge and expertise from previous effort estimation activities to assess the risks in new software projects.

As the most uncertain and complex project when compared to other types of projects, software development project is highly depend on the result of software project planning phase that helping project managers by predicting the project demands with respect to the budgeting, scheduling, and the allocation of resources. The two main activities in software project planning are effort estimation and risk assessment which has to be executed together because the accuracy of the effort estimation is highly dependent on the size, nature, and number of project risks, which are inherent in a particular software project.

In the early stages of a software development life cycle, effort estimation plays a critical role in helping project managers predict the demands with respect to the budgeting, scheduling, and the allocation of resources. In this situation, the ideal estimation calculation should provide an approximate value figure, which will consist of a base estimation value plus a contingency allowance value, which will cover the risks and assumptions necessary for particular estimation calculations. However, most software effort estimation methodologies, which include the COCOMO model, provide a fixed effort estimate value instead of an approximate value, and consequently the existing effort estimation approach has failed to become a trusted reference for project manager due to the problem in estimation accuracy. This paper introduces the Fuzzy-ExCOM Model, the Software Project Risk Assessment and Effort Contingency Model based on a COCOMO cost factors, which provides the project risk identification and contingency allowance to complement the effort estimation value based on identified project risk and software size. The proposed model also integrates the effort estimation and risk assessment activities because these activities are integral parts of the initial software project planning phase and the accuracy of the effort estimates depends heavily on the nature and level of the risks that are inherent in the software project. A validation of this model using a project data sets shows that the new model provides a higher level of effort prediction performance compared to the existing COCOMO-II effort estimation approach.

Software estimation is a tedious and daunting task in project management and software development. Software estimators are notorious in predicting software effort and they have been struggling in the past decades to provide new models to enhance software estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. This paper presents a novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams. A fuzzy logic approach is used to calibrate the productivity factor in the regression model. Moreover, a multilayer perceptron (MLP) neural network model was developed to predict software effort based on the software size and team productivity. Experiments show that the proposed approach outperforms the original UCP model. Furthermore, a comparison between the MLP and log-linear regression models was conducted based on the size of the projects. Results demonstrate that the MLP model can surpass the regression model when small projects are used, but the log-linear regression model gives better results when estimating larger projects.