Many Machine Learning (ML) algorithms are in-herently random in nature-executing them
using the same inputs may lead to slightly different results across different runs. Such …

Many machine learning (ML) algorithms are inherently random–multiple executions using
the same inputs may produce slightly different results each time. Randomness impacts how …

Testing Machine Learning (ML) projects is challenging due to inherent non-determinism of
various ML algorithms and the lack of reliable ways to compute reference results …

The stochastic nature of many Machine Learning (ML) algorithms makes testing of ML tools
and libraries challenging. ML algorithms allow a developer to control their accuracy and run …

Non-deterministic test behavior, or flakiness, is common and dreaded among developers.
Researchers have studied the issue and proposed approaches to mitigate it. However, the …

Probabilistic programming systems and machine learning frameworks like Pyro, PyMC3,
TensorFlow, and PyTorch provide scalable and efficient primitives for inference and training …

Regression testing is a critical part of software development and maintenance. It ensures
that modifications to existing software do not break existing behavior and functionality. One …

Software systems have been continuously evolved and delivered with high quality due to the
widespread adoption of automated tests. A recurring issue hurting this scenario is the …

During regression testing, developers rely on the pass or fail outcomes of tests to check
whether changes broke existing functionality. Thus, flaky tests, which nondeterministically …

Flaky tests are software tests that exhibit a seemingly random outcome (pass or fail) despite
exercising unchanged code. In this work, we examine the perceptions of software …