  In the past decades, the revolutionary advances of Machine Learning (ML) have
shown a rapid adoption of ML models into software systems of diverse types.
Such Machine Learning Software Applications (MLSAs) are gaining importance in
our daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount
importance. Several research efforts are dedicated to determining the specific
challenges we can face while adopting ML models into software systems. However,
we are aware of no research that offered a holistic view of the distribution of
those ML quality assurance challenges across the various phases of software
development life cycles (SDLC). This paper conducts an in-depth literature
review of a large volume of research papers that focused on the quality
assurance of ML models. We developed a taxonomy of MLSA quality assurance
issues by mapping the various ML adoption challenges across different phases of
SDLC. We provide recommendations and research opportunities to improve SDLC
practices based on the taxonomy. This mapping can help prioritize quality
assurance efforts of MLSAs where the adoption of ML models can be considered
crucial.
