The search space explosion problem is a long-standing challenge for search-based automated program repair (APR).
The operation space, which defines how to select appropriate mutation operators, and the ingredient space, which defines how to select appropriate code elements for fixings, are two major factors that determines the search space. Conventional approaches mainly devise fixing strategies via learning from frequent fixing patterns observed on substantial patches collected from either fixing histories or other projects. In this paper, we propose a new direction for search-based APR, that is to repair a bug via learning from how the bug was introduced instead of learning from how other bugs are frequently fixed. Our empirical study reveals that substantial mutation operators and fixing ingredients required to fix a bug can be inferred from the commit that introduced the bug. Based on the findings of our empirical study, we devised a preliminary approach based on bug-inducing commits, which is able to repair 11 new bugs that cannot be repaired by the state-of-the-art techniques. Such results demonstrate that our proposed new idea for searched-based APR is promising.