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Test smells are poor design decisions implemented in test code, which can have an impact on the effectiveness and maintainability of unit tests. Even though test smell detection tools exist, how to rank the severity of the detected smells is an open research topic. In this work, we aim at investigating the severity rating for four test smells and investigate their perceived impact on test suite maintainability by the developers. To accomplish this, we first analyzed some 1,500 open-source projects to elicit severity thresholds for commonly found test smells. Then, we conducted a study with developers to evaluate our thresholds. We found that (1) current detection rules for certain test smells are considered as too strict by the developers and (2) our newly defined severity thresholds are in line with the participants' perception of how test smells have an impact on the maintainability of a test suite. Preprint [https://doi.org/10.5281/zenodo.3744281], data and material [https://doi.org/10.5281/zenodo.3611111].
1712 Software, 10009 Department of Informatics, Empirical Software Engineering, Test Smells, Software Testing, 1706 Computer Science Applications, 005, 000 Computer science, knowledge & systems, 000 Computer science, knowledge & systems, 004
1712 Software, 10009 Department of Informatics, Empirical Software Engineering, Test Smells, Software Testing, 1706 Computer Science Applications, 005, 000 Computer science, knowledge & systems, 000 Computer science, knowledge & systems, 004
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 48 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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