Dynamic Modeling of the Effectiveness of Software Development Methods on DoD Programs
Open AccessSoftware technology products are integral to many aspects of Department of Defense (DoD) programs, providing key tools to the warfighters to ensure mission success. But the existing software development approach within the federal government is a leading source of concern, with the consensus being that it drives cost, schedule and generates unacceptable risk throughout the acquisition process (GAO, 2020). The traditional Waterfall approaches develop software similar to hardware manufacturing processes, in a series of phases. Several organizations have adopted a commercially proven Agile incremental approach, where software is produced in short iterations. But there are reports of many challenges encountered while applying incremental methods to programs in the government space (GAO, 2016). The use of agile software development practices has generated skepticism among users and decision-makers in the DoD, with concerns about their benefits and effectiveness on government projects (GAO, 2012). Using system dynamics, this praxis investigates the benefits of modern Agile Software Development Methods (SDM) as compared to traditional Waterfall SDMs more popularly adopted in DoD programs. The system dynamics model developed as part of this praxis, is calibrated using benchmark program profiles available from existing government reports and validated against real-world data sourced from a commercial organization’s database that collects performance metrics data from federal government programs. Simulation results prove the benefits of using agile SDMs in the context of development cost savings, program schedule savings, and quality improvements. The system dynamics model serves as a decision-assist tool for program managers and developers to formalize an optimized cost, schedule, and scope baseline for DoD software development programs based on the type of SDM employed. By dynamically adjusting the variables, it simulates an optimized scenario for the program and its predicted performance.
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