Applying Causal Learning to Improve Software Cost Estimation and Project Control

Getting a Handle on Software Cost

At the SEI, we have been developing modeling tools based on causal learning to identify and evaluate the issues that lead to delays, broken budgets, and other poor outcomes. Causal learning focuses on studying the relationships between a cause and its possible effects so that we can understand how we arrive at certain outcomes and how we might control them better. Research has shown that causal models for cost estimation are superior to traditional statistical models because, by identifying causal factors, proactive control of project and task outcomes becomes possible.

Over a three-year period, SEI researchers have worked to help the DoD—especially the U.S. Army and USC—to apply causal learning to control costs in software development and sustainment. We also explored how to deal with sources of uncertainty in project planning and studied traditional cost estimation models. Thanks to this work, we have been able to identify causal factors in software and systems engineering that provide ways for controlling program costs. As our research in causal learning continues to evolve, it will also help organizations better negotiate software contracts, design more effective policies and incentives, and provide more actionable information for affordability reports.

During our work with project planning, we’ve also uncovered several pitfalls that many organizations face, including poor communications, unclear planning, and ineffective hiring practices. Many organizations are often unaware of these issues, and are therefore unable to mitigate them. The SEI can help your organization identify these kinds of issues, as well as improve planning and invest your resources more effectively to chart a clear path toward achieving your program’s objectives.