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ROI Analysis of the System Architecture Virtual Integration Initiative

April 2018 Technical Report
Jörgen Hansson (University of Skovde), Steve Helton (The Boeing Company), Peter H. Feiler

This report presents an analysis of the economic effects of the System Architecture Virtual Integration approach on the development of software-reliant systems for aircraft compared to existing development paradigms.

Publisher:

Software Engineering Institute

CMU/SEI Report Number

CMU/SEI-2018-TR-002

DOI (Digital Object Identifier):
10.1184/R1/12363080.v1

Abstract

The System Architecture Virtual Integration (SAVI) initiative is a multiyear, multimillion dollar program that is developing the capability to virtually integrate systems before designs are implemented and tested on hardware. The purpose of SAVI is to develop a means of countering the costs of exponentially increasing complexity in modern aerospace software systems. The program is sponsored by the Aerospace Vehicle Systems Institute, a research center of the Texas Engineering Experiment Station, which is a member of the Texas A&M University System. This report presents an analysis of the economic effects of the SAVI approach on the development of software-reliant systems for aircraft compared to existing development paradigms. The report describes the detailed inputs and results of a return-on-investment (ROI) analysis to determine the net present value of the investment in the SAVI approach. The ROI is based on rework cost-avoidance attributed to earlier discovery of requirements errors through analysis of virtually integrated models of the embedded software system expressed in the SAE International Architecture Analysis and Design Language (AADL) standard architecture modeling language. The ROI analysis uses conservative estimates of costs and benefits, especially for those parameters that have a proven, strong correlation to overall system-development cost. The results of the analysis, in part, show that the nominal cost reduction for a system that contains 27 million source lines of code would be $2.391 billion (out of an estimated $9.176 billion), a 26.1% cost savings. The original study, reported here, had a follow-on study to validate and further refine the estimated cost savings.