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Background: Test smells indicate potential problems in the design andimplementation of automated software tests that may negatively impact test codemaintainability, coverage, and reliability. When poorly described, manual testswritten in natural language may suffer from related problems, which enabletheir analysis from the point of view of test smells. Despite the possibleprejudice to manually tested software products, little is known about testsmells in manual tests, which results in many open questions regarding theirtypes, frequency, and harm to tests written in natural language. Aims:Therefore, this study aims to contribute to a catalog of test smells for manualtests. Method: We perform a two-fold empirical strategy. First, an exploratorystudy in manual tests of three systems: the Ubuntu Operational System, theBrazilian Electronic Voting Machine, and the User Interface of a largesmartphone manufacturer. We use our findings to propose a catalog of eight testsmells and identification rules based on syntactical and morphological textanalysis, validating our catalog with 24 in-company test engineers. Second,using our proposals, we create a tool based on Natural Language Processing(NLP) to analyze the subject systems' tests, validating the results. Results:We observed the occurrence of eight test smells. A survey of 24 in-company testprofessionals showed that 80.7% agreed with our catalog definitions andexamples. Our NLP-based tool achieved a precision of 92%, recall of 95%, andf-measure of 93.5%, and its execution evidenced 13,169 occurrences of ourcataloged test smells in the analyzed systems. Conclusion: We contribute with acatalog of natural language test smells and novel detection strategies thatbetter explore the capabilities of current NLP mechanisms with promisingresults and reduced effort to analyze tests written in different idioms.

Manual Tests Do Smell! Cataloging and Identifying Natural Language Test Smells