35 © Springer Nature Switzerland AG 2022 P. Raj et al. (eds.), Blockchain, Artifcial Intelligence, and the Internet of Things, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-030-77637-4_3 Data Mining-Based Metrics for the Systematic Evaluation of Software Project Management Methodologies Patricia R. Cristaldo, Daniela López De Luise, Lucas La Pietra, Anabella De Battista, and D. Jude Hemanth 1 Introduction The management of software projects includes the fusion of science and manage- ment. It includes several aspects: direction, scope, stakeholders, risks, planning and control of activities, project requirements, and business objectives. It refers to the project manager’s abilities to manage problems related to management and technol- ogy. To help, there are numerous project management methodologies and guides on the market. Some of them are PMBOK [1], PRINCE2 [2, 3], APM [4], ISO 21500 [5], SCRUM [6, 7], KANBAN [8], and CRISP-DM [9, 10]. The correct manage- ment of projects looks for the conclusion in time and with the desired quality [11]. According to the 2018 CHAOS report, 29% of the projects respect the time, budget, characteristics, and functions required. In contrast, 37% do not respect any of these axes, and 52% of projects experience delays, exceed budget, or implement fewer requirements [12]. This is over 10% of what was reported in 2010. Likewise, the report shows a cancellation of the project without a product of 19%. P. R. Cristaldo · L. La Pietra · A. De Battista GIBD – National Technological University, Regional Branch Concepción del Uruguay, Entre Ríos, Argentina D. L. De Luise GIBD – National Technological University, Regional Branch Concepción del Uruguay, Buenos Aires, Argentina CI2S LABS, Buenos Aires, Argentina D. J. Hemanth () CI2S LABS, Buenos Aires, Argentina Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India e-mail: judehemanth@karunya.edu