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Neural network-based techniques for automated program repair are becomingincreasingly effective. Despite their success, little is known about why theysucceed or fail, and how their way of reasoning about the code to repaircompares to human developers. This paper presents the first in-depth studycomparing human and neural program repair. In particular, we investigate whatparts of the buggy code humans and two state of the art neural repair modelsfocus on. This comparison is enabled by a novel attention-tracking interfacefor human code editing, based on which we gather a dataset of 98 bug fixingsessions, and on the attention layers of neural repair models. Our results showthat the attention of the humans and both neural models often overlaps (0.35 to0.44 correlation). At the same time, the agreement between humans and modelsstill leaves room for improvement, as evidenced by the higher human-humancorrelation of 0.56. While the two models either focus mostly on the buggy lineor on the surrounding context, the developers adopt a hybrid approach thatevolves over time, where 36.8% of the attention is given to the buggy line andthe rest to the context. Overall, we find the humans to still be clearly moreeffective at finding a correct fix, with 67.3% vs. less than 3% correctlypredicted patches. The results and data of this study are a first step into adeeper understanding of the internal process of neural program repair, andoffer insights inspired by the behavior of human developers on how to furtherimprove neural repair models.

Where to Look When Repairing Code? Comparing the Attention of Neural Models and Developers