  Context: An increasing demand is observed in various domains to employ
Machine Learning (ML) for solving complex problems. ML models are implemented
as software components and deployed in Machine Learning Software Systems
(MLSSs). Problem: There is a strong need for ensuring the serving quality of
MLSSs. False or poor decisions of such systems can lead to malfunction of other
systems, significant financial losses, or even threat to human life. The
quality assurance of MLSSs is considered as a challenging task and currently is
a hot research topic. Moreover, it is important to cover all various aspects of
the quality in MLSSs. Objective: This paper aims to investigate the
characteristics of real quality issues in MLSSs from the viewpoint of
practitioners. This empirical study aims to identify a catalog of bad-practices
related to poor quality in MLSSs. Method: We plan to conduct a set of
interviews with practitioners/experts, believing that interviews are the best
method to retrieve their experience and practices when dealing with quality
issues. We expect that the catalog of issues developed at this step will also
help us later to identify the severity, root causes, and possible remedy for
quality issues of MLSSs, allowing us to develop efficient quality assurance
tools for ML models and MLSSs.
