Using Bayesian Networks to Obtain the Task's Parameters for Schedule Planning in Scrum

Planning Poker is a consensus-based technique mostly used for estimating effort or relative size of software development goals. This is applicable to estimate the size ofuser stories, developing releases and iteration plans. It is used generally with Scrum. Planning Poker has a lot of benefits, however, this method is not entirely efficient because the result is always based on the observation of an expert. This paper proposes the identification and validation of the most important factors taken into account by Scrum teams to assign complexity and importance to tasks, which are two important factors to consider for task scheduling. We define a knowledge structure that represents different aspects and the influence of each factor in the final decision. We use a Bayesian Network to co-relate these factors in order to have accurate in the estimation. The Bayesian Network give us the complexity of a task according to Fibonacci scale. Using our technique, software teams can focus in the most important tasks to obtain a quality software.