Computer Science and Engineering, Department of


First Advisor

Hamid Bagheri

Date of this Version


Document Type



Niloofar Mansoor. Formal Modeling and Analysis of a Family of Surgical Robots. MS Thesis, University of Nebraska-Lincoln, 2019.


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Computer Science, Under the Supervision of Professor Hamid Bagheri. Lincoln, Nebraska: December, 2019

Copyright 2019 Niloofar Mansoor


Safety-critical applications often use dependability cases to validate that specified properties are invariant, or to demonstrate a counterexample showing how that property might be violated. However, most dependability cases are written with a single product in mind. At the same time, software product lines (families of related software products) have been studied with the goal of modeling variability and commonality and building family-based techniques for both modeling and analysis. This thesis presents a novel approach for building an end to end dependability case for a software product line, where a property is formally modeled, a counterexample is found and then validated as a true positive via testing. There has not been such a study that we know of in an emerging safety-critical domain, specifically of robotic surgery. This thesis will detail a study on a family of surgical robots that combine hardware and software components and are highly configurable, representing over 1300 unique robots. At the same time, these robot systems are considered safety-critical and should have associated dependability cases. We conducted a case study to understand how we can bring together lightweight formal analysis, feature modeling, and testing to provide an end to end pipeline to find potential violations of important safety properties. In the process, we learned that there are some interesting and open challenges for the research community, which if solved will lead towards more dependable safety-critical cyber-physical systems.

Adviser: Hamid Bagheri