This presentation at the BR Showcase @ICSE 2026 details the trajectory and maturity of research regarding test quality in the mobile ecosystem, focusing specifically on the Dart language and the Flutter framework. The work introduces DNose, an innovative tool for the automatic detection of 14 types of test smells , whose effectiveness was validated through a rigorous empirical evaluation with 9 developers , achieving precision and recall results of 100% in several categories, such as Assertion Roulette and Magic Number. This contribution fills a critical gap, as similar tools were previously predominant only in languages such as Java and Python. The research has matured significantly, evolving from a tool proposal to a large-scale study that analyzed 4,154 Dart projects , revealing robust evidence that 60% of projects and 74% of test files are ‘infected’. With a total volume of 907,566 cataloged occurrences , the work has been refined by including co-occurrence analyses that identified ‘very strong’ correlations between smells such as Ignored Test and Duplicate Assert. This maturation now focuses on practical solutions for the community, including the planning of a specific Linter and studies on the persistence of these ‘bad smells’ over time , consolidating DNose as a profound and essential diagnostic tool to raise the standard of software engineering in Flutter/Dart development.