Performance issues may compromise user experiences, increase the cost resources, and cause field failures. One of the most prevalent performance issues is performance regression. Due to the importance and challenges in performance regression detection, prior research proposes various automated approaches that detect performance regressions. However, the performance regression detection is conducted after the system is built and deployed. Hence, large amounts of resources are still required to locate and fix performance regressions. In our paper, we propose an approach that automatically predicts whether a test would manifest performance regressions given a code commit. In particular, we extract both traditional metrics and performance-related metrics from the code changes that are associated with each test. For each commit, we build random forest classifiers that are trained from all prior commits to predict in this commit whether each test would manifest performance regression. We conduct case studies on three open-source systems (Hadoop, Cassandra and OpenJPA). Our results show that our approach can predict tests that manifest performance regressions in a commit with high AUC values (on average 0.86). Our approach can drastically reduce the testing time needed to detect performance regressions. In addition, we find that our approach could be used to detect the introduction of six out of nine real-life performance issues from the subject systems during our studied period. Finally, we find that traditional metrics that are associated with size and code change histories are the most important factors in our models. Our approach and the study results can be leveraged by practitioners to effectively cope with performance regressions in a timely and proactive manner.