Studying the effect of co-change dispersion on software quality

Software change history plays an important role in measuring software quality and predicting defects. Co-change metrics such as number of files changed together has been used as a predictor of bugs. In this study, we further investigate the impact of specific characteristics of co-change dispersion on software quality. Using statistical regression models we show that co-changes that include files from different subsystems result in more bugs than co-changes that include files only from the same subsystem. This can be used to improve bug prediction models based on co-changes.