Bayesian network model for task effort estimation in agile software development

Abstract

Even though the use of agile methods in software development is increasing, the problem of effort estimation remains quite a challenge, mostly due to the lack of many standard metrics to be used for effort prediction in plan-driven software development. The Bayesian network model presented in this paper is suitable for effort prediction in any agile method. Simple and small, with inputs that can be easily gathered, the suggested model has no practical impact on agility. This model can be used as early as possible, during the planning stage. The structure of the proposed model is defined by the authors, while the parameter estimation is automatically learned from a dataset. The data are elicited from completed agile projects of a single software company. This paper describes various statistics used to assess the precision of the model: mean magnitude of relative error, prediction at level m, accuracy (the percentage of successfully predicted instances over the total number of instances), mean absolute error, root mean squared error, relative absolute error and root relative squared error. The obtained results indicate very good prediction accuracy.