  The characterization of bug datasets is essential to support the evaluation
of automatic program repair tools. In a previous work, we manually studied
almost 400 human-written patches (bug fixes) from the Defects4J dataset and
annotated them with properties, such as repair patterns. However, manually
finding these patterns in different datasets is tedious and time-consuming. To
address this activity, we designed and implemented PPD, a detector of repair
patterns in patches, which performs source code change analysis at
abstract-syntax tree level. In this paper, we report on PPD and its evaluation
on Defects4J, where we compare the results from the automated detection with
the results from the previous manual analysis. We found that PPD has overall
precision of 91% and overall recall of 92%, and we conclude that PPD has the
potential to detect as many repair patterns as human manual analysis.
