Task allocation in distributed agile software development environment using unsupervised learning

In the paper, a novel approach for task allocation in DASD environment has been proposed. In the approach, new tasks (in the form of user – stories), are allocated to an employee, who is found to be ‘best’, on the basis of classification and rank ordering. For applying classification and rank ordering on data set of employees, Meta - Classifier Based Prediction Model (MCBPM) has been used that applied unsupervised learning. Results show that MCBPM– based task allocations provides accurate suggestions for the activity.