Testing Machine Learning (ML) projects is challenging due to inherent \textit{non-determinism} of various ML algorithms and the lack of reliable ways to compute reference results. Developers typically rely on their intuition when writing tests to check whether ML algorithms produce accurate results. However, this approach leads to conservative choices in selecting \textit{assertion bounds} for comparing actual and expected results in test assertions. Because developers want to avoid false positive failures in tests, they often set the bounds to be too loose, potentially leading to missing critical bugs.

We present FASER – the first systematic approach for balancing the trade-off between the fault-detection effectiveness and flakiness of non-deterministic tests by computing optimal \textit{assertion bounds}. FASER frames this trade-off as an optimization problem between these competing objectives by varying the assertion bound. FASER leverages 1) statistical methods to estimate the flakiness rate, and 2) mutation testing to estimate the fault-detection effectiveness. We evaluate FASER on 87 non-deterministic tests collected from 22 popular ML projects. FASER finds that 26% of the studied tests have conservative bounds and proposes tighter assertion bounds that maximizes the fault-detection effectiveness of the tests while limiting flakiness. We have sent 19 pull requests to developers and 12 pull requests have already been accepted.