Proposing a new software cost estimation model based on artificial neural networks

The precision of software project estimation such as project cost estimation, project quality estimation and project risk analysis are important issues in software project management. The ability to accurately estimate software development costs is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle. The estimates are often the least accurate, because very little detail is known about the project and the product at the beginning. The need for reliable and accurate cost predictions in software engineering is an ongoing challenge for software engineers. In this paper a novel neural network Constructive Cost Model (COCOMO) is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.