Improving the COCOMO model using a neuro-fuzzy approach

  • Authors:

  • Xishi Huang;Danny Ho;Jing Ren;Luiz F. Capretz

  • Affiliations:

  • Department of ECE, University of Western Ontario, London, Ont., Canada N6A 5B9;Toronto Design Center, Motorola Canada Ltd., Markham, Ont., Canada L6G 1B3;Department of ECE, University of Western Ontario, London, Ont., Canada N6A 5B9;Department of ECE, University of Western Ontario, London, Ont., Canada N6A 5B9

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.