# Software Project Risk Identification Based on Scrum Artifacts
## 2 Related Work
This approach promotes the reuse of software project-related knowledge and aligns with Scrum principles, highlighting the improvement in decision-making and organizational learning in software risk management. Still, the effectiveness of the recommender system again relies heavily on the quality and completeness of stored historical risk data. The authors of [31] introduced the Agile Risk Tool (ART), a lightweight risk management framework designed to minimize human effort by harnessing software agents. ART automates risk identification, assessment, and monitoring using environmental data collected from Agile projects. A significant feature of ART is its ability to dynamically react to changes in project environments through predefined risk rules. Another study [32] built on ART by proposing a goal-driven framework that decomposes the project goals into risk factors and their corresponding indicators. This framework emphasizes integrating risk management into Agile processes by monitoring critical metrics such as team skills, task ownership, and project progress. The model uses autonomous software agents to continuously manage risks, demonstrating effectiveness in case studies. There are also approaches dedicated to specific aspects of risk management in Agile projects. For example, in [33], an approach is proposed for managing risks in machine learning solution development using Agile methodologies, but it focuses on data and model quality, overlooking the broader scope of risk management. Similarly, authors of [34] present a case study-based approach for risk identification and mitigation in Agile softwarere-engineering projects, emphasizing technical debt and legacy system challenges, making it primarily suitable for re-engineering contexts. On the other hand, some approaches target only specific steps in risk management, such as decision-making [35], where fuzzy cognitive maps, mathematical modeling, and expert methods are used to dynamically analyze and mitigate risks. However, the method in Ref. [35] lacks detailed processes for risk identification and classification and does not address integration with Agile practices and tools. ART [31; 32] and similar approaches focus heavily on data-driven risk assessment, but human elements, such as team motivation, interpersonal conflicts, and leadership issues, are not always adequately addressed. These factors, essential in Agile methodologies, are difficult to quantify and can be overlooked by automated systems. Furthermore, integrating the analyzed risk management tools like [30; 31] with existing project management and development tools (e.g., Jira) can be challenging, especially when the data structures or formats do not align. Limited access to specific data due to tool compatibility issues can undermine the system's effectiveness in providing comprehensive risk assessments. A more detailed comparison of the analyzed approaches is presented in Table 1. Our proposed framework builds on prior research by combining structured risk classification with automated monitoring of Scrum artifacts and their attributes. This approach aims to address gaps in risk management, such as the lack of formalized processes and the burden of manual data collection, by leveraging automation to improve proactive risk management in Scrum projects.