Effort Estimation Based on Collaborative Filtering
Effort estimation methods are one of the important tools for project managers in controlling human resources of ongoing or future software projects. The estimations require historical project data including process and product metrics that characterize past projects. Practically, in using the estimation methods, it is a problem that the historical project data frequently contain substantial missing values. In this paper, we propose an effort estimation method based on Collaborative Filtering for solving the problem. Collaborative Filtering has been developed in information retrieval researchers, as one of the estimation techniques using defective data, i.e. data having substantial missing values. The proposed method first evaluates similarity between a target (ongoing) project and each past project, using vector based similarity computation equation. Then it predicts the effort of the target project with the weighted sum of the efforts of past similar projects. We conducted an experimental case study to evaluate the estimation performance of the proposed method. The proposed method showed better performance than the conventional regression method when the data had substantial missing values.
KeywordsNeighborhood Size Collaborative Filter Mean Absolute Error Effort Estimation Listwise Deletion
Unable to display preview. Download preview PDF.
- 1.Albrecht, A., Gaffney, J.: Software Function, Source Lines of Code, and Development Effort Prediction. IEEE Trans. on Software Eng. 9(6), 83–92 (1979)Google Scholar
- 3.Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. of the 14th Conf. on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
- 5.Briand, L., El Eman, K., Wieczorek, I.: Explaining the Cost of European Space and Military Projects. Proc. Int’l Conf. Software Eng. 1(1), 61–88 (1996)Google Scholar
- 6.Conte, S.D., Dunsmore, H.E., Shen, V.Y.: Software Engineering Metrics and Models. The Benjamin/Cummings Publishing Company, Inc., Menlo Park (1986)Google Scholar
- 13.Rahhal, S., Madhavji, N.: An Effort Estimation Model for Implementing ISO 9001. In: Proc. of the 2nd IEEE Int’l Software Eng. Standards Symp., pp.278–286 (1995)Google Scholar
- 14.Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proc. ACM Conf. on Computer Supported Cooperative Work (CSCW 1994), Chapel Hill, North Carolina, United States, pp. 175–186 (1994)Google Scholar
- 16.Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: Proc. 10th International World Wide Web Conference (WWW10), Hong Kong, pp. 285–295 (2001)Google Scholar
- 17.Shepperd, M., Schofield, C.: Estimating Software Project Effort Using Analogies. IEEE Trans. on Software Eng. 23(12), 76–743 (1997)Google Scholar