  Diverse studies have analyzed the quality of automatically generated test
cases by using test smells as the main quality attribute. But recent work
reported that generated tests may suffer a number of quality issues not
necessarily considered in previous studies. Little is known about these issues
and their frequency within generated tests. In this paper, we report on a
manual analysis of an external dataset consisting of 2,340 automatically
generated tests. This analysis aimed at detecting new quality issues, not
covered by past recognized test smells. We use thematic analysis to group and
categorize the new quality issues found. As a result, we propose a taxonomy of
13 new quality issues grouped in four categories. We also report on the
frequency of these new quality issues within the dataset and present eight
recommendations that test generators may consider to improve the quality and
usefulness of the automatically generated tests.
