Predicting defects using change genealogies

摘要

When analyzing version histories, researchers traditionally focused on single events: e.g. the change that causes a bug, the fix that resolves an issue. Sometimes however, there are indirect effects that count: Changing a module may lead to plenty of follow-up modifications in other places, making the initial change having an impact on those later changes. To this end, we group changes into change genealogies, graphs of changes reflecting their mutual dependencies and influences and develop new metrics to capture the spatial and temporal influence of changes. In this paper, we show that change genealogies offer good classification models when identifying defective source files: With a median precision of 73% and a median recall of 76%, change genealogy defect prediction models not only show better classification accuracies as models based on code complexity, but can also outperform classification models based on code dependency network metrics.