Models constrain the range of possible behaviors de£ned for a domain. When parts of a model are uncertain, the possible behaviors may be a data cloud: i.e. an overwhelming range of possibilities that bewilder an analyst. Faced with large data clouds, it is hard to demonstrate that any particular decision leads to a particular outcome.Even if we can't make de£nite decisions from such models, it is possible to £nd decisions that reduce the variance of values within a data cloud. Also, it is possible to change the range of these future behaviors such that the cloud condenses to some improved mode.Our approach uses two tools. Firstly, a model simulator is constructed that knows the range of possible values for uncertain parameters. Secondly, the TAR2 treatment learner uses the output from the simulator to incrementally learn better constraints. In our incremental treatment learning cycle, users review newly discovered treatments before they are added to a growing pool of constraints used by the model simulator.
Chung et al. have proposed a graphical model that captures the interdependencies between design alternatives in terms of synergy and trade-offs. This model can assist in identifying quality/risk trade-offs early in the lifecycle of software development, such as architectural design and testing process choices. The Chung et al. method is an analysis framework only: their technique does not include an execution or analysis module. This paper presents a simulation tool developed to analyze such a model, and techniques to facilitate decision making by reducing the space of options worth considering. Our techniques combine Monte Carlo simulations to generate options with a machine learner to determine which option yields the most/least favorable outcome. Experiments based on the above methodology were performed on two case studies, and the results showed that treatment learning successfully pinpointed the key attributes among uncertainties in our test domains.
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