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ASE 2000

Practical Large Scale What-If Queries: Case Studies with Software Risk Assessment

Tim Menzies, Erik Sinsel

DOI 10.1109/ase.2000.873661dblp

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.

BibTeX
@inproceedings{Menzies-Sinsel:ASE00,
  author    = {Tim Menzies and
               Erik Sinsel},
  title     = {Practical Large Scale {What-If} Queries: Case Studies with Software Risk Assessment},
  booktitle = {ASE},
  pages     = {165-},
  publisher = {{IEEE} Computer Society},
  year      = {2000},
}

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