Date of Award

5-2023

Degree Type

Thesis

Degree Name

Departmental Honors

Department

Electrical and Computer Engineering

Abstract

This Honors Capstone was proposed as a method to develop a greater understanding of modern model-checking tools. To do this the student chose to analyze and compare the results of the tools Prism, Storm, and Stamina. To evaluate the effectiveness of each tool, comparisons were made of the results for running each tool on a simplified communication network.

The simplified communication network model used was a CTMC (Continuous-Time Markov Chain) model that employed correct signal transitions and erroneous bit flipping transitions. This was done in an effort to simulate possible errors and faults that can occur between a provider and a receiver. Tool effectiveness was measured by examining the probability and time that the model-checking tool took to verify the communication model’s probability of entering a specific state. The communication model initially starts with all signals at zero, and each signal changes to facilitate a handshaking protocol.

In addition to the standard tools that were being tested, it was desired to additionally make a comparison against these tools using an altered form of SSA (Stochastic Simulation Algorithm) being designed in a proposed senior project. This other simultaneous project did in fact achieve and complete the implementation of this altered SSA. The senior project experienced issues in attempting to gain results that could be fairly compared to the other model-checking tools used in this capstone design. Therefore, the results of the altered SSA are not addressed in this report due to the lack of fair comparison.

There were multiple initial assumptions made in the capstone design regarding the communication model. The results of using the model-checking tools either confirmed or disproved these assumptions. Educated guesses were used to predict the most probable error states of the communication model as well as for the states with the lowest probability of entry. These error state predictions were disproved when other states were shown to have far lower entry probability than anticipated.

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Faculty Mentor

Chris Winstead

Departmental Honors Advisor

Todd Moon