Regression testing is an important part of software development projects, and it is used to ensure software quality. Traditionally, a regression test focuses primarily on functional correctness of a modified program and is examined only when it fails, meaning it found a fault that would have otherwise been undetected. For certain application domains, regression tests for non-functional quality aspects such as performance, security, and usability could be just as important. However, those regression tests are much more costly and difficult to create, and thus many applications lack adequate non-functional regression test coverage. This adds risk of regressions in these areas as changes are made over time. In this research, we propose using metrics from passing test cases to predict quality aspects of the software beyond the traditional focus of regression tests. Our industrial case study shows that metrics such as test response time from functional regression tests are good predictors of which product areas are likely to contain certain types of non-functional performance faults. Furthermore, we show that this prediction can be improved through environmental perturbation such as the use of synthetic volume datasets or data size variation.