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 …

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 …

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 …

Background: Recently, machine and deep learning (ML/DL) algorithms have been
increasingly adopted in many software systems. Due to their inductive nature, ensuring the …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

Rapid progress in Machine Learning (ML) has seen a swift translation to real world
commercial deployment. While research and development of ML applications have …

When software is modified, during development and maintenance, it is regression tested to
provide confidence that the changes did not introduce unexpected errors and that new …

Nowadays, we are witnessing a wide adoption of Machine learning (ML) models in many
software systems. They are even being tested in safety-critical systems, thanks to recent …

Regression testing---rerunning tests on each code version to detect newly-broken
functionality---is important and widely practiced. But, regression testing is costly due to the …