Reducing the Uncertainty of Agile Software Development Using a Random Forest Classification Algorithm

Author:

Wińska EwelinaORCID,Kot EsteraORCID,Dąbrowski WłodzimierzORCID

Publisher

Springer International Publishing

Reference16 articles.

1. Braga, P.L., Oliveira, A.L.I.: Software effort estimation using machine learning techniques with robust confidence intervals. In: 19th IEEE International Conference on Tools with Artificial Intelligence (2007)

2. Patel, B.N., Prajapati, S.G., Lakhtaria, K.I.: Efficient classification of data using decision tree. Bonfring Int. J. Data Min. 2(1), 06–12 (2012)

3. Bhatia, S., Attri, V.K.: Implementing decision tree for software development effort estimation of software project. International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, no. 5, May 2015

4. Kumari, S.: Comparison and analysis of different software cost estimation methods. (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 4, no. 1 (2013)

5. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Springer Science + Business Media, 2 March 2006

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