Date of Award:

5-2026

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Steve Petruzza

Committee

Steve Petruzza

Committee

Mario Harper

Committee

Yang Shi

Abstract

Probabilistic model checking is a critical method for analyzing systems characterized by uncertainty, such as communication protocols, randomized algorithms, and biochemical networks. While formal verification tools provide precise numerical data about these systems, interpreting these results is often limited by large state space, high-dimensional state space and time dependent evolution. Current tools typically output raw numerical data, offering limited support for intuitively understanding the time-dependent behavior of a model. This research presents an interactive visualization framework designed to bridge the gap between complex numerical analysis and human intuition. The framework integrates coordinated visual interfaces, including lower-dimensional state-space projections and synchronized timelines, allowing researchers to observe how system probabilities and variable relationships evolve over time. By transforming abstract mathematical data into a dynamic visual environment, this tool enables analysts to more effectively evaluate, navigate, and debug the refinements of complex stochastic models. We demonstrate the utility of this framework through three exploratory use cases, highlighting its capacity to make formal verification results more transparent and actionable for system designers and researchers.

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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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