Tests are important artifacts in the software development process. Testing activities such as test automation, test maintenance, and test suite optimization mainly rely on an in-depth understanding of test specifications. The manual process of writing testspecifications in natural language can create many different quality issues such as ambiguous, incomplete, redundant, or inconsistent test cases. Nowadays, the concept of test smells is proposed by several researchers to be used as indicators of low-qualityattributes in test specifications. Quality assurance processes for test specifications often rely on manual reviews to detect these smells. The manual process of detecting these smells is considered time consuming and costly. However, there is currently no work that implements a comprehensive quality model that classifies and identifies these smells by using a systematic strategy. As a result, there is a need for machine-supported analytical measures that decrease the time and effort needed to detect these smells manually, especially when it comes to reviewing and validating large test specifications.This study aims to investigate which natural language smell metrics implemented in NALABS can be found in test specifications and to measure the sufficiency of those smellmetrics. It also aims to extend these smell metrics by exploring, proposing, or combining with new bad smell metrics to cover more aspects of natural language test quality. The results of the study show that the smell metrics exists in real-world test specifications and can uncover many potential quality issues by assisting test designers in identifying certain types of quality issues pertaining to for example the understandability and complexity of test specifications. Moreover, the results show thatthe list of smell metrics implemented in NALABS is incomplete and can be extended to cover more aspects of test quality.