Dealing with Uncertainty in Binary Logistic Regression Fault-proneness Models
Luigi Lavazza, Sandro Morasca · 2019
Background Binary Logistic Regression is widely used in Empirical Software Engineering to build estimation models, e.g., fault-proneness models, which estimate the probability that a given module is faulty, based on some measures of the module. Fault-proneness models are then used to build faultiness model, i.e., models that estimate whether a given module is faulty or non-faulty.