An Analysis of the State of the Art of Machine Learning for Risk Assessment in Software Projects (S)

Abstract

Risk management is one of the ten knowledge areas discussed in the Project Management Body of Knowledge (PMBOK), which serves as a guide that should be followed to increase the chances of project success. The popularity of research regarding the application of risk management in software projects has been consistently growing in recent years, particularly with the application of machine learning techniques to help identify risk levels or risk factors of a project before the project development begins, with the intent of improving the likelihood of success of software projects.This paper provides an overview of various concepts related to risk and risk management in software projects, including traditional techniques used to identify and control risks in software projects, as well as machine learning techniques and methods which have been applied to provide better estimates and classification of the risk levels and risk factors that can be encountered during the development of a software project. The paper also presents an analysis of machine learning oriented risk management studies and experiments found in the literature as a way of identifying the type of inputs and outputs, as well as frequent algorithms used in this research area.

Managing Risks in Agile Methods: a Systematic Literature Mapping

“…Esteki et al [8], integrates Scrum with the PRINCE2 delivery layer; Schön et al [9] standardizes risk increasing transparency in the context of multidisciplinary projects; Hayat et al [10] estimate the impact of risk and convert it into risk detection and control actions. Risk management in software projects has even attracted the application of Machine Learning (ML) aiming to identify or predict risks before project development starts [11].…”