Practical large scale what-if queries: case studies with software risk assessment

Abstract

When a lack of data inhibits decision-making, large-scale what-if queries can be conducted over the uncertain parameter ranges. Such queries can generate an overwhelming amount of data. We describe a general method for understanding that data. Large-scale what-if queries can guide Monte Carlo simulations of a model. Machine learning can then be used to summarize the output. The summarization is an ensemble of decision trees. The TARZAN system [so-called because it swings through (or searches) the decision trees] can poll the ensemble looking for majority conclusions regarding what factors change the classifications of the data. TARZAN can succinctly present the results from very large what-if queries. For example, in one of the studies presented, we can view the significant features from 10/sup 9/ what-if queries on half a page.