Estimating user stories’ complexity and importance in scrum with Bayesian networks

Abstract

Planning Poker is a light-weight technique for estimating the size of user stories, in face-to-face interaction and discussions. Planning Poker is generally used 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 a new model to estimate the complexity and importance of user stories based on Planning Poker in the context of Scrum. The goal of this work is to facilitate the decision-making of newbie developers when they estimate user stories’ parameters. Hence, the decision of each member would be clearer to understand than when the complexity is taken as a whole. We use a Bayesian Network to co-relate factors to have accurate in the estimation. The Bayesian Network gives the complexity of a user story, according to the Fibonacci scale used in Planning Poker.