Causal Models for Software Cost Prediction and Control (video)
November 2019 • Video
Michael D. Konrad, William Nichols, Robert W. Stoddard, David Zubrow
Researchers at CMU SEI collaborated with other researchers to apply causal learning to learn how to control costs in software development and sustainment.
Software Engineering Institute
Cost estimation inaccuracy continues to be cited as a dominant factor in DoD cost overruns. Research has shown causal models are superior to traditional statistical models because, by identifying truly causal factors, proactive control of project and task outcomes is possible. In this work, we expect to develop causal models, including structural equation models (SEMs) that provide a basis for (1) calculating the effort, schedule, and quality results of software projects under different scenarios (e.g., traditional vs. agile) and (2) estimating the results of interventions applied to a project in response to a change in requirements (e.g., a change in mission) or to help bring it back on track toward achieving cost, schedule, and technical requirements.