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The International Conference on Artificial Intelligence and Applied Mathematics in Engineering

ICAIAME 2019: Artificial Intelligence and Applied Mathematics in Engineering Problems pp 295–319Cite as

A Novel Model for Risk Estimation in Software Projects Using Artificial Neural Network

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 43))

Abstract

Software projects generally involve more risks due to unexpected negative results. Therefore, the risks encountered in software projects should be detected and analyzed on time, and effective precautions should be taken in order to complete the projects successfully. The aim of this study was to estimate the deviations that may occur in the software project outputs according to risk factors by using artificial neural networks (ANNs). Thus we aimed to minimize loses that may occur in project processes with the developed model. Firstly, a comprehensive and effective list of risk factors was created. Later, a checklist form was prepared for Team Members and Managers. The data collected include general project data and risk factors, and these are the inputs of the model. The outputs of the model are the deviations in the project outputs. MATLAB package program was utilized to develop the model. The performance of the model was measured according to Regression Values and Mean-Squared Error. The model obtained has forty-five inputs, one hidden layer that has fifteen neurons, and five outputs (45-15-5). In addition, the training-R, testing-R, and MSE values of the model were found as 0.9978, 0.9935, and 0.001, respectively. It is seen that the estimation results obtained with the model using the real project data coincide with the actual results largely and the error rates were also very low (close to zero). The experimental results clearly revealed that model performance is high, and it is very effective to use ANNs in risk estimation processes for software projects.

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Acknowledgments

The authors would like to thank experts in technoparks at Gazi University, at Hacettepe University, at Middle East Technical University, and at Ankara University for their very helpful suggestions during obtaining data about their software projects.

Author information

Authors and Affiliations

  1. Department of Management Information Systems, Karadeniz Technical University, Trabzon, Turkey

    M. Hanefi Calp

  2. Department of Computer Engineering, Gazi University, Ankara, Turkey

    M. Ali Akcayol

Authors
  1. M. Hanefi Calp
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  2. M. Ali Akcayol
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Corresponding author

Correspondence to M. Hanefi Calp .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

  2. Department of Computer Engineering, Faculty of Engineering, Suleyman Demirel University, Isparta, Isparta, Turkey

    Utku Kose

Appendix

Appendix

Risk Identification Form

Project Scope Mobile App. () Network App. () Game App. () Communication App. () Other (…)

Position Analyst () Experts () Team Leader () Project Manager () General Manager () Developer () Testing Personnel ()

Project Time 0-6 months () 6–12 months () 12–24 months () 24–48 months () more than 48 months ()

Project Budget (TL) Under 50000 () 50000 TL -100000 TL () 100 000-250 000 () 250000-500000 () 500000 over ()

Personnel Number Less than 10 () 10-30 () 30-50 () 50-100 () more than 100 ()

Is there a deviation in the project duration? No () Yes () …%

(The time difference between specified delivery date in the beginning with actual date)

Is there a deviation in the budget? No Yes () …%

(The difference between specified budget in the beginning with budget at the project delivery date)

Is there a deviation in the number of personnel? No Yes () …%

(The difference between specified personnel number in the beginning with personnel number at the project delivery date)

Is there a deviation from the target? No () Yes () …%

(The completion status of the project work packages specified in the beginning)

Is there a deviation in project success? No () Yes () …%

(The completion of project on time, with determined budget, number of personnel and target)

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Calp, M.H., Akcayol, M.A. (2020). A Novel Model for Risk Estimation in Software Projects Using Artificial Neural Network. In: Hemanth, D., Kose, U. (eds) Artificial Intelligence and Applied Mathematics in Engineering Problems. ICAIAME 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-36178-5_23

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