Causal Models for Software Cost Prediction & Control (video)
November 2019 • Video
Watch SEI researchers Dr. Michael Konrad, Dr. William Nichols, Mr. Robert Stoddard, and Dr. David Zubrow discuss recent results from applying causal learning to control and predict software cost.
Software Engineering Institute
In this work, we expect to develop causal models, including structural equation models (SEMs), that provide a basis for calculating the effort, schedule, and quality results of software projects under different scenarios (e.g., traditional vs. agile), estimating the results of interventions applied to a project in response to a change in requirements (e.g., a change in mission), or helping to bring it back on track toward achieving cost, schedule, and technical requirements. Thus, an immediate benefit of this work is the identification of causal factors that provide a basis for controlling program costs. A longer-term benefit is the use of causal models in negotiating software contracts, designing policy and incentives, and informing could/should-cost and affordability efforts.