Graphical Visualization of Risk Assessment for Effective Risk Management during Software Development Process

Success of any IT industry depends on the success rate of their projects, which in turn depends on several factors such as cost, time, and availability of resources. These factors formulate the risk areas, which needs to be addressed in a proactive way. The rudimentary objective of risk management is to circumvent the possibility of their occurrence by identifying the risks, preparing the contingency plans and mitigation plans in order to reduce the consequences of the risks. Hence, effective risk management becomes one of the imperative challenges in any organization, which if deemed in an apt way assures the continued sustainability of the organization in the high-end competitive environment. This paper provides visualization of risk assessment through a graphical model. Further, the matrix representation of the risk assessment aids the project personnel to identify all the risks, comprehend their frequency and probability of their occurrence. In addition, the graphical model enables one to analyze the impact of identified risks and henceforth to assign their priorities. This mode of representation of risk assessment factors helps the organization in accurate prediction of success rate of the project.

💡 Research Summary

This paper, titled “Graphical Visualization of Risk Assessment for Effective Risk Management during Software Development Process,” addresses the critical challenge of risk management in software projects. It argues that the sustainability and success of IT organizations hinge on their ability to proactively manage risks related to cost, schedule, resources, and technology. Moving beyond traditional experience-based methods, the paper proposes a structured, visual-mathematical model to enhance the objectivity and effectiveness of risk assessment.

The core of the proposal lies in defining and relating key risk assessment factors. It identifies three independent variables: Risk Type (N1 – e.g., technological, schedule, personnel), Probability of Occurrence (N2), and Frequency of Occurrence (N3). These factors are used to assess any given Risk (N). The paper then introduces two dependent variables: Risk Impact (N4) and Risk Priority (N5).

The primary contribution is the graphical visualization of the relationships between these factors using a directed graph. The model clearly illustrates that the three independent factors (N1, N2, N3) directly influence both the Risk (N) itself and its Impact (N4). The Risk Impact (N4), in turn, is the sole determinant of the Risk Priority (N5). This establishes a transitive relationship where, for instance, Risk Type indirectly influences Priority through its effect on Impact.

To enable formal analysis, this relational structure is translated into an adjacency matrix (M_R). The matrix representation concretely shows the dependencies and, importantly, highlights the transitive nature of the relationships (e.g., N1 -> N4 -> N5). The authors suggest that for more complex systems with numerous factors, algorithms like Floyd-Warshall could be applied to this matrix to compute transitive closures and uncover all indirect influence paths.

The paper also provides practical context through sample classifications. It lists common risk types at a coarse level and discusses how to categorize probability (as a percentage) and frequency (e.g., Frequent, Occasional, Seldom). This bridges the conceptual model with practical risk identification processes that involve stakeholders throughout the project lifecycle.

In conclusion, the research posits that a mathematical and visual perspective on risk assessment can lead to more accurate predictions of project success compared to purely supposition-based approaches. The proposed graphical model and matrix scheme aim to empower project personnel to systematically analyze risks, understand their interrelationships, and ultimately assign more informed priorities. The paper acknowledges that the model is primarily applicable to known and identified risks and indicates that future work will explore the impact analysis of these representations in greater depth.