We present PRECFIX, a pragmatic approach targeting large-scale industrial codebase and making recommendations based on previously observed debugging activities. PRECFIX collects defect-patch pairs from development histories, performs clustering, and extracts generic reusable patching patterns as recommendations. We con- ducted experiments on the Alibaba codebase involving diverse defect patterns. We managed to extract 3K templates of defect-patch pairs, which have been successfully applied to perform path recommendation. Our approach is able to make recommendations within milliseconds and achieves a false positive rate of 22% confirmed by manual review. The majority of the interviewed developers appreciated PRECFIX, which has been rolled out to Alibaba to support various critical businesses.