Abstract
The inability of engineering managers to accurately predict schedule impact after a software integration test error results in increased schedule risk based on underestimation of the schedule delay. This research outlines how to effectively apply knowledge integration to provide engineering managers with a simple approach to support decisions by incorporating evidence-based recommendations for schedule impact that considers both legacy and current data. Knowledge integration is used to merge known software integration challenges with error reports from U.S. Army software integration test events to define Naïve Bayes Model features. This approach results in a model that predicts schedule delay caused by a software integration error more accurately than existing approaches.
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