Against the Mainstream in Bug Prediction

Bug prediction is a technique used to estimate the most bug-prone entities in software systems. Bug prediction approaches vary in many design options, such as dependent variables, independent variables, and machine learning models. Choosing the right combination of design options to build an effective bug predictor is hard. Previous studies do not consider this complexity and draw conclusions based on fewerthan-necessary experiments. We argue that each software project is unique from the perspective of its development process. Consequently, metrics and AI models perform differently on different projects, in the context of bug prediction. We confirm our hypothesis empirically by running different bug predictors on different systems. We show that no single bug prediction configuration works globally on all projects and, thus, previous bug prediction findings cannot generalize.