In this blog post, I describe the SEI Software Cost Prediction and Control (abbreviated as SCOPE) project, where we apply causal-modeling algorithms and tools to a large volume of project data to identify, measure, and test causality. The post builds on research undertaken with Bill Nichols and Anandi Hira at the SEI, and my former colleagues David Zubrow, Robert Stoddard, and Sarah Sheard. We sought to identify some causes of project outcomes, such as cost and schedule overruns, so that the cost of acquiring and operating software-reliant systems and their growing capability is predictable and controllable.
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