Abstract
Execution traces can be overwhelmingly large. To reduce their size, sampling techniques, especially the ones based on random sampling, have been extensively used. Random sampling, however, may result in samples that are not representative of the original trace. We propose a trace sampling framework based on stratified sampling that not only reduces the size of a trace but also results in a sample that is representative of the original trace by ensuring that the desired characteristics of an execution are distributed similarly in both the sampled and the original trace.
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