Model-based analysis is a common technique to identify incorrect behavioral composition of complex, safety-critical systems, such as robotics systems. However, creating structural and behavioral models for hundreds of software components manually is often a labor-intensive and error-prone process. I propose an approach to infer behavioral models for components of systems based on the Robot Operating System (ROS), the most popular framework for robotics systems, using a combination of static and dynamic analysis by exploiting assumptions about the usage of the ROS framework. This work is a contribution towards making well-proven and powerful but infrequently used methods of model-based analysis more accessible and economical in practice to make robotics systems more reliable and safe.

I am PhD student in Software Engineering at Carnegie Mellon University in Pittsburgh since August 2018. I am co-advised by Claire Le Goues and David Garlan.

I am mainly interested in simplifying the development of complex software systems. My areas of research include software architecture, model-based analysis of quality-attributes for robotics systems, and static code analysis.