Predicting the Unpredictable: Using Monte Carlo Simulation to Predict Project Completion Date

  • Lucas ColucciEmail author
  • Raphael Albino
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 680)


If you work with software development you will probably face two important, but not always convergent, aspects: scope and delivery cadence. The process of aligning the expectations of product increment and team throughput is usually arduous but, when this happens, it improves the chances of project success. Stakeholders frequently want the project done faster than it is possible for us to do it. And then, when they ask the date on which we will finish the work, we never have the right answer. In the last two years, while working with different projects at Plataformatec, we have been trying to solve that problem in many different ways: mean throughput, linear regression and even manually adjusting our predictions. However, all of them had their drawbacks. This paper presents what we think will be the best approach to forecast project deadline: Monte Carlo Simulation. We explain how it works, how to apply it in a project and how you can benefit from using it.


Monte carlo simulation Statistics Project management Metrics Agile methodology Prediction Forecast 


  1. 1.
    Osborne, J.W., Waters, E.: Four assumptions of multiple regression that researchers should always test. Pract. Assess. Res. Eval. 8, 1–5 (2002)Google Scholar
  2. 2.
    Raychaudhuri, S.: Introduction to Monte Carlo simulation. In: Proceedings of the 2008 Winter Simulation Conference, pp. 91–100 (2008)Google Scholar
  3. 3.
    Brezonik, L., Majer, C.: Grid information services for distributed resource sharing. In: Proceedings of the SQAMIA 2016: 5th Workshop of Software Quality, Analysis, Monitoring, Improvement, and Applications, Budapest, Hungary (2016)Google Scholar
  4. 4.
    Usman, M., Mendes, E., Weidt, F., Britto, R.: Effort estimation in agile software development: a systematic literature review. In: Proceedings of the 10th International Conference on Predictive Models in Software Engineering, Turin, Italy (2014)Google Scholar
  5. 5.
    Magennis, T.: Managing software development risk using modeling and monte carlo simulation. In: Proceedings of the Lean Software and Systems Consortium 2012 Conference in Boston, Massachusetts, USA (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.PlataformatecSão PauloBrazil

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