Software Delivery Risk Management: Application of Bayesian Networks in Agile Software Development

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Abstract

The information technology industry cannot be imagined without large- or small-scale projects. They are implemented to develop systems enabling key business processes and improving performance and enterprise resource management. However, projects often experience various difficulties during their execution. These problems are usually related to the three objectives of the project – costs, quality and deadline. A way these challenges can be solved is project risk management. However, not always the main problems and their influencing factors can be easily identified. Usually there is a need for a more profound analysis of the problem situation. In this paper, we propose the use of a Bayesian Network concept for quantitative risk management in agile projects. The Bayesian Network is explored using a case study focusing on a project that faces difficulties during the software delivery process. We explain why an agile risk analysis is needed and assess the potential risk factors, which may occur during the project. Thereafter, we design the Bayesian Network to capture the actual problem situation and make suggestions how to improve the delivery process based on the measures to be taken to reduce the occurrence of project risks.

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  • [1] M. Li M. Huang F. Shu and J. Li “A risk-driven method for eXtreme programming release planning” in Proc. of the 28th int. conf. on Software engineering Shanghai China 2006 pp. 423–430. http://dx.doi.org/10.1145/1134285.1134344

  • [2] A. Nagy M. Njima and L. Mkrtchyan “A Bayesian Based Method for Agile Software Development Release Planning and Project Health Monitoring” in 2nd Int. Conf. Intelligent Networking and Collaborative Systems (INCOS) Nov. 24–26 2010 Thessaloniki Greece. IEEE 2010. http://dx.doi.org/10.1109/incos.2010.99

  • [3] S. W. Ambler and M. Lines Disciplined Agile Delivery: A Practitioner’s Guide to Agile Software Delivery in the Enterprise Indianapolis: IBM Press 2012.

  • [4] M. Perukusich G. Soares H. Almeida and A. Perkusich “A procedure to detect problems of processes in software development projects using Bayesian networks” Expert Systems with Applications vol. 42 pp. 437–450 Jan. 2015. http://dx.doi.org/10.1016/j.eswa.2014.08.015

  • [5] S. Mohanarajah and M. A. Jabar “An Improved Adaptive and Dynamic Hybrid Agile Methodology to Enhance Software Project Success Deliveries” Journal of Theoretical & Applied Information Technology vol. 75 pp. 301–325 May 2015.

  • [6] P. Serrador and J. K. Pinto “Does Agile work? – A quantitative analysis of agile project success” International Journal of Project Management vol. 33 pp. 1040–1051 July 2015. http://dx.doi.org/10.1016/j.ijproman.2015.01.006

  • [7] M. Perukusich H. Almeida and A. Perkusich “A model to detect problems on scrum-based software development projects” in Proceedings of the 28th Annual ACM Symposium on Applied Computing Coimbra Portugal 2013 pp. 1037–1042. http://dx.doi.org/10.1145/2480362.2480560

  • [8] H. Xiaocong and K. Ling “A Risk Management Decision Support System for Project Management Based on Bayesian Network” Information Management and Engineering (ICIME) 2010 The 2nd IEEE International Conference April 16–18 2010 Chengdu China. IEEE 2010. http://dx.doi.org/10.1109/ICIME.2010.5478061

  • [9] X. Bo L. Xie-lin and Z. Li-guo “Accident Management Reinforcing with Qualitative and Quantitative Analysis” in 2013 International Conference on Management Science & Engineering (20th) July 17–19 2013 Harbin P.R.China. IEEE 2013. http://dx.doi.org/10.1109/ICMSE.2013.6586589

  • [10] S. Wagner “A Bayesian network approach to assess and predict software quality using activity-based quality models” Information and Software Technology vol. 52 pp. 1230–1241 Nov. 2010. http://dx.doi.org/10.1016/j.infsof.2010.03.016

  • [11] J. Dhlamini I. Nhamu and A. Kachepa “Intelligent risk management tools for software development” in Proceedings of the 2009 Annual Conference of the Southern African Computer Lecturers’ Association: SACLA ’09 Eastern Cape South Africa 2009 pp. 33–40. http://dx.doi.org/10.1145/1562741.1562745

  • [12] Z. Zhang G. Rao J. Cao and L. Zhang “Software Process Risk Measurement Model based on Bayesian Network” in 2014 5th `IEEE International Conference Software Engineering and Service Science (ICSESS) June 27–29 2014 Beijing China. IEEE 2014. http://dx.doi.org/10.1109/ICSESS.2014.6933510

  • [13] I. Khuankrue and W. Rivepiboon “Model of cross-culture risk prediction base on Bayesian belief networks for software project” in 2012 International Conference on Innovation Management and Technology Research (ICIMTR2012) Malacca Malaysia 2012 pp. 560–565. http://dx.doi.org/10.1109/ICIMTR.2012.6236458

  • [14] S. V. Shrivastava and U. Rathod “Categorization of risk factors for distributed agile projects” Information and Software Technology vol. 58 pp. 373–387 Feb. 2015. http://dx.doi.org/10.1016/j.infsof.2014.07.007

  • [15] V. G. Venkatesh S. Rathi and S. Patwa “Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using Interpretive structural modeling” Journal of Retailing and Consumer Services vol. 26 pp. 153–167 Sept. 2015. http://dx.doi.org/10.1016/j.jretconser.2015.06.001

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