# Software Project Risk Identification Based on Scrum Artifacts
## 5 Discussion and Conclusions  
A key strength of the proposed framework is its adaptability. Agile teams employ various approaches to risk identification, including the selection of tracked project data, thresholds, and risk registration principles. The framework accommodates this variability, allowing teams to tailor its structure and components to their specific context. This flexibility is essential, as no two Scrum projects are identical, and a one-size-fits-all approach to risk identification is insufficient. Furthermore, the framework enables teams to identify and document risky deviations with minimal effort by automating data collection from project management tools such as Jira. The proposed structure, which links project artifacts and attributes with risk classifications, allows for the real-time assessment of project status. It helps to identify potential risks by applying threshold values defined by the team. By leveraging these tools for data gathering and analysis, teams can continuously monitor the project situation and detect emerging risks immediately, preventing risks from escalating. It is advisable for the team and its manager to implement the framework at an early stage of the project. This facilitates data collection and subsequent analysis. The team should focus on the key factors influencing the selection of the appropriate framework configuration. Initially, it is recommended to include all artifacts, emphasizing comprehensible and pertinent attributes to the team. The same approach applies to risk management. If the team lacks a clear understanding of the risks, their identification may lead to confusion and dissatisfaction. The list of attributes and risks can be expanded as the project progresses,  
Figure 15: Heatmap of detected risks by sprint. as the primary elements will have been stabilized, enabling broader project monitoring. However, reducing the number of attributes or risks is not recommended, as long-term mutual correlations may uncover latent risks. For complex projects with multiple teams, it is recommended that each team maintains its own set of attributes and monitored risks. Aggregating these elements across teams may introduce challenges in tracing root causes and complicate the development of risk mitigation strategies. However, the framework has limitations. It relies on accurate and timely data imports from tools such as Jira. Delayed or inconsistent imports can result in incomplete risk assessments and missed critical risks. Moreover, the effectiveness of the framework depends on the team's understanding of the principles of risk identification and their active participation in the process. Furthermore, the reliance on threshold values for risk detection presents challenges, as improperly calibrated thresholds may generate false positives or fail to identify significant risks. Experimental evaluations of the framework in several projects demonstrated its potential to improve team-wide risk awareness throughout the project lifecycle. For the teams, their most unexpected and important realization was an underestimation of the impact of long-term risks. The gathered historical project execution data facilitated the identification of persistent issues that, while appearing negligible within the scope of a single sprint, revealed their influence over extended periods. Including all team members in the risk analysis process eliminated the practice of confining risk evaluation and decision-making to senior members, fostering a collaborative approach to risk identification. Future research will focus on improving the synchronization with the Jira project management tool. This will involve enabling the capability to flag risks directly within a project execution environment and convert them into actionable tasks. Additionally, efforts will be directed toward expanding the range of calculated attributes to encompass a broader project ecosystem. Another critical area identified during the experiments is the interdependence between attributes and risks.