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.

翻译：已有研究通过测试异味作为主要质量属性分析了自动生成测试用例的质量。但近期工作表明，生成测试可能遭受以往研究中未充分考虑的一系列质量问题。学界对这些问题的本质及其在生成测试中的出现频率知之甚少。本文对包含2,340个自动生成测试的外部数据集进行了人工分析，旨在检测以往公认测试异味未涵盖的新质量问题。我们采用主题分析法对发现的新质量问题进行分类归纳，最终提出包含四类13项新质量问题的分类体系。同时报告了这些新质量问题在数据集中的出现频率，并给出八项建议供测试生成器参考，以提升自动生成测试的质量与实用性。