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At MSR18 in Gothenburg, I presented [my work](https://arxiv.org/abs/1804.02443) on using Bayesian inference to set software metrics thresholds. We want to set thresholds because for many software metrics, like coupling between objects (CBO), a single, global metric value ("all software objects with this value or below are maintainable") is nonsensical, if only because programming language choice is important. So we want to tailor threshold values to some contextually relevant value (e.g., perhaps all Java code should be X or less). The question I answered is how we do the tailoring, given some contextual features.