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.
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López-Martínez, J., Juárez-Ramírez, R., Ramírez-Noriega, A., Licea, G., Navarro-Almanza, R. (2017). Estimating User Stories’ Complexity and Importance in Scrum with Bayesian Networks. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-56535-4_21
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DOI: https://doi.org/10.1007/978-3-319-56535-4_21
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