  Test smells can pose difficulties during testing activities, such as poor
maintainability, non-deterministic behavior, and incomplete verification.
Existing research has extensively addressed test smells in automated software
tests but little attention has been given to smells in natural language tests.
While some research has identified and catalogued such smells, there is a lack
of systematic approaches for their removal. Consequently, there is also a lack
of tools to automatically identify and remove natural language test smells.
This paper introduces a catalog of transformations designed to remove seven
natural language test smells and a companion tool implemented using Natural
Language Processing (NLP) techniques. Our work aims to enhance the quality and
reliability of natural language tests during software development. The research
employs a two-fold empirical strategy to evaluate its contributions. First, a
survey involving 15 software testing professionals assesses the acceptance and
usefulness of the catalog's transformations. Second, an empirical study
evaluates our tool to remove natural language test smells by analyzing a sample
of real-practice tests from the Ubuntu OS. The results indicate that software
testing professionals find the transformations valuable. Additionally, the
automated tool demonstrates a good level of precision, as evidenced by a
F-Measure rate of 83.70%
