Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


School of Computing

Committee Member

Dr. John D. McGregor, Committee Chair

Committee Member

Dr. Brian Malloy, Committee Co-Chair

Committee Member

Dr. Murali Sitaraman

Committee Member

Dr. Amy Apon


When commissioning a system, a myriad of potential designs can successfully fulfill the system's goals. Deciding among the candidate designs requires an understanding of how the design affects the system's quality attributes and how much effort is needed to realize the design. The difficulty of the process compounds if the system to be designed includes dynamic run-time self- adaptivity, the ability for the system to self-modify its architecture at run-time in response to either external or internal stimuli, as the type and location of the dynamic self-adaptivity within the architecture must be co-decided. In this proposal, we introduce a Decision Support System, which contains a new Dynamic Software Product Line-centric cost / effort estimation technique, the Structured Intuitive Model for Dynamic Adaptive System Economics (SIMDASE), that will allow system designers / architects to select the most appropriate design for systems where the candidates can be structured as a Dynamic Software Product Line. We will focus on using the Decision Support System to select designs for a system where at least one component of the system is a low-level embedded system for use within the Internet of Things (IoT), particularly embedded systems whose purpose is to exist as "things" (either intelligent sensors or actuators). The Decision Support System we introduce is a multi-step process that begins with a high- level system architecture generated from the system requirements and goals. Candidate designs that can fulfill all goals / requirements of the high-level architecture are selected. Each design is then annotated using SIMDASE so that the effort, risk, cost and return on investment that can be expected from the realization of the design(s) can be compared in order to select the best design for a given organization.