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Which components of a large software system are the most defect-prone? In a study on a large SAP Java system, the authors evaluated and compared a number of defect predictors, based on code features such as complexity metrics, static error detectors, change frequency, or component imports, thus replicating a number of earlier case studies in an industrial context. They found the overall predictive power to be lower than expected; still, the resulting regression models successfully predicted 50 - 60% of the 20% most defect-prone components.
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