Automatic Identification of Bug-Introducing Changes

Bug-fixes are widely used for predicting bugs or finding risky parts of software. However, a bug-fix does not contain information about the change that initially introduced a bug. Such bug-introducing changes can help identify important properties of software bugs such as correlated factors or causalities. For example, they reveal which developers or what kinds of source code changes introduce more bugs. In contrast to bug-fixes that are relatively easy to obtain, the extraction of bug-introducing changes is challenging. In this paper, the authors present algorithms to automatically and accurately identify bug-introducing changes. They remove false positives and false negatives by using annotation graphs, by ignoring non-semantic source code changes, and outlier fixes.