To contain costs, it is essential to understand which factors drive costs over the longer term and can be controlled. In studies of software development, as a research community, we have not done an adequate job of differentiating causal influences from noncausal statistical correlations. In this podcast, Mike Konrad and Bob Stoddard discuss the use of an approach known as causal learning that can help the Department of Defense identify which factors cause software costs to escalate and, therefore, serve as a better basis for guidance on how to intervene to better control costs.
Michael Konrad is a principal researcher at the SEI, where he was worked for 30 years. Currently, he is applying causal and machine learning to problems such as: understanding software lifecycle costs; innovating jet engine reliability, and classifying human activities in video. Konrad earned his Ph.D. in mathematics from Ohio University in 1978. Konrad shares a provisional patent on a measure of error propagation complexity based on work performed for the FAA in 2016.
Robert Stoddard is a principal researcher at the Carnegie Mellon University Software Engineering Institute. Stoddard's research includes machine/causal learning, applied statistics, Bayesian probabilistic modeling, Six Sigma, and quality/reliability engineering. Stoddard earned a master of science degree in systems management and significant doctoral progress in reliability and quality management. Stoddard is a fellow of the American Society for Quality and senior member of the IEEE. Stoddard holds five ASQ certifications and is a Motorola-certified Six Sigma Master Black Belt.