Title :
Practical large scale what-if queries: case studies with software risk assessment
Author :
Menzies, Tim ; Sinsel, Erik
Author_Institution :
WVU IV&V Fac., NASA, Fairmont, WV, USA
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 109 what-if queries on half a page
Keywords :
Monte Carlo methods; computer aided software engineering; data mining; decision support systems; decision trees; large-scale systems; learning (artificial intelligence); query processing; safety; software cost estimation; COCOMO-II; Monte Carlo simulations; TARZAN system; case studies; data classification; data understanding; decision support system; decision tree ensemble polling; decision-making; ensemble learning; large-scale what-if queries; machine learning; majority conclusions; output summarization; significant features; software risk assessment; uncertain parameter ranges; Computer aided software engineering; Costs; Databases; Decision making; Large-scale systems; Machine learning; NASA; Open source software; Project management; Risk management;
Conference_Titel :
Automated Software Engineering, 2000. Proceedings ASE 2000. The Fifteenth IEEE International Conference on
Conference_Location :
Grenoble
Print_ISBN :
0-7695-0710-7
DOI :
10.1109/ASE.2000.873661
