Application in Effort Estimation of Collaborative Filtering

Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. An approach to estimation effort based upon analogy researched in the paper. Collaborative Filtering has been developed in information retrieval researchers successfully which recommends items based on other user's reference in historical data set. Effort estimation based on Collaborative Filtering is researched. The similar projects set are found from historical projects set using the method for document similarity, and then effort is estimated using the weighted sum of the efforts in k-nearest neighbors. The method is applied in an experimental case to evaluate the effort estimation, and the result shows the accuracy of estimation may arrive to 90%.