Bayesian Hierarchical Modelling for Tailoring Metric Thresholds

Software is highly contextual. While there are cross-cutting `global' lessons, individual software projects exhibit many `local' properties. This data heterogeneity makes drawing local conclusions from global data dangerous. A key research challenge is to construct locally accurate prediction models that are informed by global characteristics and data volumes. Previous work has tackled this problem using clustering and transfer learning approaches, which identify locally similar characteristics. This paper applies a simpler approach known as Bayesian hierarchical modeling. We show that hierarchical modeling supports cross-project comparisons, while preserving local context. To demonstrate the approach, we conduct a conceptual replication of an existing study on setting software metrics thresholds. Our emerging results show our hierarchical model reduces model prediction error compared to a global approach by up to 50%.Comment: Short paper, published at MSR '18: 15th International Conference on Mining Software Repositories May 28--29, 2018, Gothenburg, Swede

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Studies on the Mode of Binding of Histamine in the Tissues.

The effect of tonicity on the rate and amount of histamine released from several in vitro preparations was studied. In hypertonic (1.2M) solutions of sucrose or mannitol, basic histamine liberators released significantly less histamine from dog liver particles, isolated mast cells, perfused guinea pig lungs, and perfused cat paws, than they did in isotonic solutions. When surface-active compounds were used as histamine liberators, no significant differences were found in the amount of histamine released in the two kinds of solution. [...