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This paper is about understanding the nature of bug fixing by analyzingthousands of bug fix transactions of software repositories. It then places thislearned knowledge in the context of automated program repair. We give extensiveempirical results on the nature of human bug fixes at a large scale and a finegranularity with abstract syntax tree differencing. We set up mathematicalreasoning on the search space of automated repair and the time to navigatethrough it. By applying our method on 14 repositories of Java software and89,993 versioning transactions, we show that not all probabilistic repairmodels are equivalent.

Mining Software Repair Models for Reasoning on the Search Space of Automated Program Fixing