The problem of automated feedback generation for introductory programming assignments (IPAs) has attracted significant attention with the increasing demand for programming education. While existing approaches, like Refactory, that employ the “block-by-block” repair strategy have produced promising results on this task, they suffer from two limitations. First, Refactory randomly applies refactoring and mutation operations to the correct and buggy programs, respectively, to align their control-flow structures (CFSs), which, however, has a relatively low success rate and often complicates the original repairing tasks. Second, Refactory generates a repair for each basic block of the buggy program when its semantics differs from the counterpart in the correct program, which, however, ignores the different roles the basic blocks play in the programs and often produces unnecessary repairs. To overcome these limitations, we propose the Brafar approach to feedback generation for IPAs. The core innovation of Brafar lies in its novel bidirectional refactoring algorithm and coarse-to-fine fault localization. The former aligns the CFSs of the buggy and correct programs by applying semantics-preserving refactoring operations to both programs in a guided manner, while the latter identifies basic blocks that really need repairing based on the semantics of their enclosing statements and themselves. In our experimental evaluation on 1783 real-life incorrect student submissions from a publicly available dataset, Brafar significantly outperformed Refactory, generating correct repairs for more incorrect programs with smaller patch sizes in a shorter time.