# Software Project Risk Identification Based on Scrum Artifacts
###### Abstract  
Risk identification, as a foundational step in risk management, is essential for the success of software development projects, including those using the Scrum methodology. However, risk management in Scrum often lacks structured practices, relying instead on the iterative and collaborative nature of the methodology. This paper presents a novel risk identification framework tailored for Scrum projects, leveraging Scrum artifacts and their associated attributes to enhance risk detection and analysis. The framework encompasses automated data collection from project management environments, such as Jira, thus minimizing the effort required from the project team for risk-related data gathering. By monitoring deviations in artifact attributes and applying predefined thresholds, the framework facilitates the detection of risks at any point in the project lifecycle. Experimental evaluation across three distinct Scrum projects demonstrated the framework's applicability in identifying a wide range of risks, including long-term ones. The proposed framework contributes to Agile project management by offering a structured, automated approach to risk identification, fostering a culture of proactive risk management within Scrum teams. risk management; scrum; agile project management; risk identification 2025  
15824  
Lina  
often incorporate additional artifacts such as requirement models, design documents, test plans, etc. [6; 7; 8]. The possibility of expanding the list of Scrum artifacts can provide a team with a broader perspective on how to identify project risks related to them [8; 9]. Therefore, we propose a hierarchical set of Scrum artifacts that includes the three key artifacts and their sub-artifacts, providing a more detailed perspective on the relationship between artifacts and risks. Furthermore, Scrum artifacts can be linked to track project progress information, which we refer to as attributes (also known as metrics [10] or parameters [11]), which further enhances the risk identification process. The status of an artifact can be monitored through its attribute values, with deviations from expected values signaling potential risk. Project progress information can be manually entered or obtained from the project management environment, enabling calculation of derived values. This process automates risk monitoring and enables timely detection of risks during project execution. The aim of this paper is to facilitate automated risk identification in Agile software development (Scrum) projects using information from Scrum project artifacts, their attributes, and the proposed project risk classification. One of the primary objectives is to minimize the requirements for human contribution in risk detection, both from the team's and the client's perspectives. This is accomplished by maximizing the automation of project attribute data collection from project management environments, such as Jira (version 10.0.1) [12]. Our approach encompasses risk identification not only within individual sprints but also long-term risk tracking based on attribute values calculated across multiple sprints (e.g., standard deviation of team velocity). We also on risks in software product development, facilitating stakeholder collaboration, team communication, and Scrum ceremonies to facilitate the development process. The proposed methodology also supports the manual entry of attribute values, which is useful when automated data collection is not feasible. The proposed framework was evaluated over 12 months in three Scrum projects. The results demonstrate its applicability in enabling teams to identify a wide range of risks, including long-term ones. The remainder of this paper is organized as follows: Section 2 reviews related work in the domain of risk identification and management in Agile projects. Section 3 introduces the proposed risk identification framework and the methodology for its application. Section 4 provides a comprehensive experimental evaluation of the proposed methodology.