Using Bayesian Networks to Support Managing Technological Risk on Software Projects

  • Emanuel Dantas UFCG
  • Ademar Sousa Neto UFCG
  • Mirko Perkusich UFCG
  • Hyggo Almeida UFCG
  • Angelo Perkusich UFCG


Risk management is essential in software project management. It includes activities such as identifying, measuring and monitoring risks. The literature presents different approaches to support software risk management. In particular, the researchers popularly used Bayesian Networks because they can be learned from data or elicited from domain experts. Even though the literature presents many Bayesian networks (BN) for software risk management, none focus on technological risk factors. Given this, this paper presents a BN for managing risks of software projects and the results of a static validation performed through a focus group with eight practitioners. As a result, the practitioners agreed that our proposed to manage technological risks of software projects using BN is valuable and easy to use. Given the successful results, we concluded that the proposed solution is promising.

Palavras-chave: Risk Management, Technological Risk, Bayesian Network


Ieva Ancveire, Ilze Gailite, and Gailite. 2015. Software Delivery Risk Management: Application of Bayesian Networks in Agile Software Development. Information Technology and Management Science 18, 1 (2015), 62–69.

Henri Barki, Suzanne Rivard, and Jean Talbot. 1993. Toward an assessment of software development risk. Journal of management information systems (1993).

Irad Ben-Gal, Ayala Shani, André Gohr, Jan Grau, Sigal Arviv, Armin Shmilovici, and Posch. 2005. Identification of transcription factor binding sites with variableorder Bayesian networks. Bioinformatics (2005).

Irad Ben-Gal, Ayala Shani, André Gohr, Jan Grau, Sigal Arviv, Armin Shmilovici, Stefan Posch, and Ivo Grosse. 2005. Identification of transcription factor binding sites with variable-order Bayesian networks. Bioinformatics 21, 11 (2005).

John Bowers and Alireza Khorakian. 2014. Integrating risk management in the innovation project. European Journal of innovation management (2014).

Saad Yasser Chadli, Ali Idri, José Luis Fernández-Alemán, Joaquín Nicolás Ros, and Ambrosio Toval. 2016. Identifying risks of software project management in Global Software Development: An integrative framework. In IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA).

Fred D Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly (1989), 319–340.

Ellen A Drost et al. 2011. Validity and reliability in social science research. Education Research and perspectives 38, 1 (2011), 105.

Norman Fenton, William Marsh, Martin Neil, Patrick Cates, Simon Forey, and Manesh Tailor. 2004. Making resource decisions for software projects. In Proceedings. 26th International Conference on Software Engineering. IEEE, 397–406.

Norman Fenton and Martin Neil. 2018. Risk assessment and decision analysis with Bayesian networks. Crc Press.

Nir Friedman and Daphne Koller. 2003. Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Machine learning 50, 1 (2003), 95–125.

Barney G Glaser and Anselm L Strauss. 2017. Discovery of grounded theory: Strategies for qualitative research. Routledge.

Tony Gorschek, Per Garre, Stig Larsson, and Claes Wohlin. 2006. A model for technology transfer in practice. IEEE software 23, 6 (2006), 88–95.

Kim Heldman. 2010. Project manager’s spotlight on risk management. John Wiley & Sons.

David Hinde. 2018. PRINCE2 Study Guide: 2017 Update. John Wiley & Sons.

ISO Central Secretary. 2018. ISO 31000: risk management–Guidelines. Standard. International Organization for Standardization, Geneva, CH.

Ankur Joshi, Saket Kale, Satish Chandel, and D Kumar Pal. 2015. Likert scale: Explored and explained. Current Journal of Applied Science and Technology (2015).

Vahid Khodakarami and Abdollah Abdi. 2014. Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items. International Journal of Project Management 32, 7 (2014), 1233–1245.

Jyrki Kontio, Johanna Bragge, and Laura Lehtola. 2008. The focus group method as an empirical tool in software engineering. In Guide to advanced empirical software engineering. Springer, 93–116.

Chandan Kumar and Dilip Kumar Yadav. 2017. Software defects estimation using metrics of early phases of software development life cycle. International Journal of System Assurance Engineering and Management 8, 4 (2017), 2109–2117.

Emilia Mendes. 2014. Expert-Based Knowledge Engineering of Bayesian Networks. In Practitioner’s Knowledge Representation. Springer, 73–105.

Emilia Mendes. 2014. Practitioner’s knowledge representation: a pathway to improve software effort estimation. Springer Science & Business.

Jislane SS Menezes, Danilo GA Ramos, and Michel S Soares. [n.d.]. On Criteria to Choose a Content Management System: A Technology Acceptance Model Approach. ([n. d.]).

Cinzia Muriana and Giovanni Vizzini. 2017. Project risk management: A deterministic quantitative technique for assessment and mitigation. International Journal of Project Management 35, 3 (2017), 320–340.

Ngoc-Tuan Nguyen, Quyet-Thang Huynh, and Thi-Huong-Giang Vu. 2018. A Bayesian Critical Path Method for Managing Common Risks in Software Project Scheduling. In Proceedings of the Ninth International Symposium on Information and Communication Technology. 382–388.

V Nikolova, Ju Kuporov, and G Rodionov. 2015. Risk management of innovation projects in the context of globalization. International Journal of Economics and Financial Issues 5, 3S (2015).

Mirko Perkusich, Lenardo Chaves e Silva, Alexandre Costa, Felipe Ramos, Renata Saraiva, Arthur Freire, Ednaldo Dilorenzo, Emanuel Dantas, Danilo Santos, Kyller Gorgônio, et al. 2020. Intelligent software engineering in the context of agile software development: A systematic literature review. Information and Software Technology 119 (2020), 106241.

Mirko Perkusich, Gustavo Soares, Hyggo Almeida, and Angelo Perkusich. 2015. A procedure to detect problems of processes in software development projects using Bayesian networks. Expert Systems with Applications 42, 1 (2015), 437–450.

PMI. 2018. A guide to the project management body of knowledge (PMBOK guide).

Md Forhad Rabbi and Khan Olid Bin Mannan. 2008. A review of software risk management for selection of best tools and techniques. In 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing. IEEE, 773–778.

Per Runeson and Martin Höst. 2009. Guidelines for conducting and reporting case study research in software engineering. Empirical software engineering 14, 2 (2009), 131–164.

Robert S Russell and Bernard W Taylor-Iii. 2008. Operations management along the supply chain. John Wiley & Sons.

Renata M Saraiva, Mirko Perkusich, Hyggo O Almeida, and Angelo Perkusich. 2017. A Process to Calculate the Uncertainty of Software Metrics-based Models Using Bayesian Networks.. In SEKE. 467–472.

Ashish B Sasankar and Vinay Chavan. 2011. SWOT analysis of software development process models. International Journal of Computer Science Issues (IJCSI) 8, 5 (2011).

Forrest Shull, Janice Singer, and Dag IK Sjøberg. 2007. Guide to advanced empirical software engineering. Springer.

Jeff Sutherland and Ken Schwaber. 2013. The Scrum Guide. Acessado em: 01-06-2020.

June M Verner, O Pearl Brereton, Barbara A Kitchenham, Mahmood Turner, and Mahmood Niazi. 2014. Risks and risk mitigation in global software development: A tertiary study. Information and Software Technology (2014).

Linda G Wallace and Steven D Sheetz. 2014. The adoption of software measures: A technology acceptance model (TAM) perspective. Information & Management 51, 2 (2014).
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DANTAS, Emanuel; SOUSA NETO, Ademar; PERKUSICH, Mirko; ALMEIDA, Hyggo; PERKUSICH, Angelo. Using Bayesian Networks to Support Managing Technological Risk on Software Projects. In: WORKSHOP BRASILEIRO DE ENGENHARIA DE SOFTWARE INTELIGENTE (ISE), 1. , 2021, Joinville. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 1-6. DOI: