Tese de mestrado, Engenharia Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasAutomated test generation tools (such as EvoSuite) typically aim to maximize code
coverage. However, they frequently disregard non-coverage aspects that can be relevant
for testers, such as the quality of the generated tests. Therefore, automatically generated
tests are often affected by a set of test-specific bad programming practices that may hinder
the quality of both test and production code, i.e., test smells. Given that other researchers
have successfully integrated non-coverage quality metrics into EvoSuite, we decided to
extend the EvoSuite tool such that the generated test code is smell-free. To this aim, we
compiled 54 test smells from several sources and selected 16 smells that are relevant to the
context of this work. We then augmented the tool with the respective test smell metrics
and investigated the diffusion of the selected smells and the distribution of the metrics.
Finally, we implemented an approach to optimize the test smell metrics as secondary
criteria. After establishing the optimal configuration to optimize as secondary criteria
(which we used throughout the remainder of the study), we conducted an empirical study
to assess whether the tests became significantly less smelly. Furthermore, we studied
how the proposed metrics affect the fault detection effectiveness, coverage, and size of
the generated tests. Our study revealed that the proposed approach reduces the overall
smelliness of the generated tests; in particular, the diffusion of the “Indirect Testing” and
“Unrelated Assertions” smells improved considerably. Moreover, our approach improved
the smelliness of the tests generated by EvoSuite without compromising the code coverage
or fault detection effectiveness. The size and length of the generated tests were also not
affected by the new secondary criteria
