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11.4.1 Generating Predictive Models Using Decision Trees and Neural Networks for Large‐Scale Systems Engineering

Systems engineering must tackle the challenges of computational systems that are increasingly large‐scale and software‐intensive in terms of system size, component breadth and maturity, and development heterogeneity. This research describes and empirically evaluates techniques for generating predictive models for enabling large‐scale system development and management. We describe two types of metric‐driven decision models, decision trees and neural networks, which classify software components in large systems according to their likelihood of having user‐specified properties such as high fault‐proneness or high development effort. The metric‐driven decision models enable coarse‐grain analysis of large‐scale multi‐component heterogeneous systems, and they identify high‐payoff areas for directing the application of fine‐grain analysis techniques for fault detection or redesign. The decision models serve as metric integration mechanisms that enable the synergistic use of numerous metrics simultaneously and integrate measurements collected by development tools or infrastructure. Model generation techniques automatically generate the decision models to calibrate them to new projects and organizations.